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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.04958">arXiv:2304.04958</a> <span> [<a href="https://arxiv.org/pdf/2304.04958">pdf</a>, <a href="https://arxiv.org/format/2304.04958">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> AROW: V2X-based Automated Right-of-Way Algorithm for Cooperative Intersection Management </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shah%2C+G">Ghayoor Shah</a>, <a href="/search/cs?searchtype=author&query=Tian%2C+D">Danyang Tian</a>, <a href="/search/cs?searchtype=author&query=Moradi-Pari%2C+E">Ehsan Moradi-Pari</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.04958v2-abstract-short" style="display: inline;"> Research in Cooperative Intersection Management (CIM), utilizing Vehicle-to-Everything (V2X) communication among Connected and/or Autonomous Vehicles (CAVs), is crucial for enhancing intersection safety and driving experience. CAVs can transceive basic and/or advanced safety information, thereby improving situational awareness at intersections. The focus of this study is on unsignalized intersecti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04958v2-abstract-full').style.display = 'inline'; document.getElementById('2304.04958v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.04958v2-abstract-full" style="display: none;"> Research in Cooperative Intersection Management (CIM), utilizing Vehicle-to-Everything (V2X) communication among Connected and/or Autonomous Vehicles (CAVs), is crucial for enhancing intersection safety and driving experience. CAVs can transceive basic and/or advanced safety information, thereby improving situational awareness at intersections. The focus of this study is on unsignalized intersections, particularly Stop Controlled-Intersections (SC-Is), where one of the main reasons involving crashes is the ambiguity among CAVs in SC-I crossing priority upon arriving at similar time intervals. Numerous studies have been performed on CIM for unsignalized intersections based on centralized and distributed systems in the presence and absence of Road-Side Unit (RSU), respectively. However, most of these studies are focused towards replacing SC-I where the scheduler provides spatio-temporal or sequence-based reservation to CAVs, or where it controls CAVs via kinematic commands. These methods cause CAVs to arrive at the intersection at non-conflicting times and cross without stopping. This logic is severely limited in real-world mixed traffic comprising human drivers where kinematic commands and other reservations cannot be implemented as intended. Thus, given the existence of SC-Is and mixed traffic, it is significant to develop CIM systems incorporating SC-I rules while assigning crossing priorities and resolving the related ambiguity. In this regard, we propose a distributed Automated Right-of-Way (AROW) algorithm for CIM to assign explicit SC-I crossing turns to CAVs and mitigate hazardous scenarios due to ambiguity towards crossing priority. The algorithm is validated with extensive experiments for its functionality, scalability, and robustness towards CAV non-compliance, and it outperforms the current solutions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04958v2-abstract-full').style.display = 'none'; document.getElementById('2304.04958v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.08076">arXiv:2303.08076</a> <span> [<a href="https://arxiv.org/pdf/2303.08076">pdf</a>, <a href="https://arxiv.org/format/2303.08076">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Optimized Control-Centric Communication in Cooperative Adaptive Cruise Control Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Razzaghpour%2C+M">Mahdi Razzaghpour</a>, <a href="/search/cs?searchtype=author&query=Shahram%2C+S">Shahriar Shahram</a>, <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Zaman%2C+M">Mahdi Zaman</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.08076v2-abstract-short" style="display: inline;"> In this study, we explore an innovative approach to enhance cooperative driving in vehicle platooning systems through the use of vehicle-to-everything (V2X) communication technologies. As Connected and Autonomous Vehicles (CAVs) integrate into increasingly dense traffic networks, the challenge of efficiently managing communication resources becomes crucial. Our focus is on optimizing communication… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.08076v2-abstract-full').style.display = 'inline'; document.getElementById('2303.08076v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.08076v2-abstract-full" style="display: none;"> In this study, we explore an innovative approach to enhance cooperative driving in vehicle platooning systems through the use of vehicle-to-everything (V2X) communication technologies. As Connected and Autonomous Vehicles (CAVs) integrate into increasingly dense traffic networks, the challenge of efficiently managing communication resources becomes crucial. Our focus is on optimizing communication strategies to support the growing network of interconnected vehicles without compromising traffic safety and efficiency. We introduce a novel control-aware communication framework designed to reduce communication overhead while maintaining essential performance standards in vehicle platoons. This method pivots from traditional periodic communication to more adaptable aperiodic or event-triggered schemes. Additionally, we integrate Model-Based Communication (MBC) to enhance vehicle perception under suboptimal communication conditions. By merging control-aware communication with MBC, our approach effectively controls vehicle platoons, striking a balance between communication resource conservation and control performance. The results show a marked decrease in communication frequency by 47\%, with minimal impact on control accuracy, such as less than 1\% variation in speed. Extensive simulations validate the effectiveness of our combined approach in managing communication and control in vehicle platoons, offering a promising solution for future cooperative driving systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.08076v2-abstract-full').style.display = 'none'; document.getElementById('2303.08076v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.13984">arXiv:2212.13984</a> <span> [<a href="https://arxiv.org/pdf/2212.13984">pdf</a>, <a href="https://arxiv.org/format/2212.13984">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Performance Analysis of V2I Zone Activation and Scalability for C-V2X Transactional Services </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zaman%2C+M">Mahdi Zaman</a>, <a href="/search/cs?searchtype=author&query=Saifuddin%2C+M">Md Saifuddin</a>, <a href="/search/cs?searchtype=author&query=Razzaghpour%2C+M">Mahdi Razzaghpour</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.13984v1-abstract-short" style="display: inline;"> Cellular-V2X (C-V2X) enables communication between vehicles and other transportation entities over the 5.9GHz spectrum. C-V2X utilizes direct communication mode for safety packet broadcasts (through the usage of periodic basic safety messages) while leaving sufficient room in the resource pool for advanced service applications. While many such ITS applications are under development, it is crucial… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.13984v1-abstract-full').style.display = 'inline'; document.getElementById('2212.13984v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.13984v1-abstract-full" style="display: none;"> Cellular-V2X (C-V2X) enables communication between vehicles and other transportation entities over the 5.9GHz spectrum. C-V2X utilizes direct communication mode for safety packet broadcasts (through the usage of periodic basic safety messages) while leaving sufficient room in the resource pool for advanced service applications. While many such ITS applications are under development, it is crucial to identify and optimize the relevant network parameters. In this paper, we envision an infrastructure-assisted transaction procedure entirely carried out by C-V2X, and we optimize it in terms of the service parameters. To achieve the service utility of a transaction class, two C-V2X entities require a successive exchange of multiple messages. With this notion, our proposed application prototype can be generalized for any vehicular service to establish connections on-the-fly. We identify suitable activation zones for vehicles and assess their impact on service efficiency. The results show a variety of potential service and parameter settings that can be appropriate for different use-cases, laying the foundation for subsequent studies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.13984v1-abstract-full').style.display = 'none'; document.getElementById('2212.13984v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.12819">arXiv:2212.12819</a> <span> [<a href="https://arxiv.org/pdf/2212.12819">pdf</a>, <a href="https://arxiv.org/format/2212.12819">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Raftari%2C+A">Arash Raftari</a>, <a href="/search/cs?searchtype=author&query=Mahjoub%2C+H+N">Hossein Nourkhiz Mahjoub</a>, <a href="/search/cs?searchtype=author&query=Razzaghpour%2C+M">Mahdi Razzaghpour</a>, <a href="/search/cs?searchtype=author&query=Mahmud%2C+S+K">Syed K. Mahmud</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.12819v1-abstract-short" style="display: inline;"> Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inhe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.12819v1-abstract-full').style.display = 'inline'; document.getElementById('2212.12819v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.12819v1-abstract-full" style="display: none;"> Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.12819v1-abstract-full').style.display = 'none'; document.getElementById('2212.12819v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.11963">arXiv:2211.11963</a> <span> [<a href="https://arxiv.org/pdf/2211.11963">pdf</a>, <a href="https://arxiv.org/format/2211.11963">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Razzaghpour%2C+M">Mahdi Razzaghpour</a>, <a href="/search/cs?searchtype=author&query=Pedarsani%2C+R">Ramtin Pedarsani</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.11963v1-abstract-short" style="display: inline;"> It is expected that autonomous vehicles(AVs) and heterogeneous human-driven vehicles(HVs) will coexist on the same road. The safety and reliability of AVs will depend on their social awareness and their ability to engage in complex social interactions in a socially accepted manner. However, AVs are still inefficient in terms of cooperating with HVs and struggle to understand and adapt to human beh… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.11963v1-abstract-full').style.display = 'inline'; document.getElementById('2211.11963v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.11963v1-abstract-full" style="display: none;"> It is expected that autonomous vehicles(AVs) and heterogeneous human-driven vehicles(HVs) will coexist on the same road. The safety and reliability of AVs will depend on their social awareness and their ability to engage in complex social interactions in a socially accepted manner. However, AVs are still inefficient in terms of cooperating with HVs and struggle to understand and adapt to human behavior, which is particularly challenging in mixed autonomy. In a road shared by AVs and HVs, the social preferences or individual traits of HVs are unknown to the AVs and different from AVs, which are expected to follow a policy, HVs are particularly difficult to forecast since they do not necessarily follow a stationary policy. To address these challenges, we frame the mixed-autonomy problem as a multi-agent reinforcement learning (MARL) problem and propose an approach that allows AVs to learn the decision-making of HVs implicitly from experience, account for all vehicles' interests, and safely adapt to other traffic situations. In contrast with existing works, we quantify AVs' social preferences and propose a distributed reward structure that introduces altruism into their decision-making process, allowing the altruistic AVs to learn to establish coalitions and influence the behavior of HVs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.11963v1-abstract-full').style.display = 'none'; document.getElementById('2211.11963v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: substantial text overlap with arXiv:2202.00881</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.10585">arXiv:2211.10585</a> <span> [<a href="https://arxiv.org/pdf/2211.10585">pdf</a>, <a href="https://arxiv.org/format/2211.10585">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Razzaghpour%2C+M">Mahdi Razzaghpour</a>, <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+G">Ghayoor Shah</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.10585v1-abstract-short" style="display: inline;"> Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles (HVs) is challenging, as HVs continuously update their policies in response to AVs. In order to navigate safely in the presence of complex AV-HV social interactions, the AVs must learn to predict these changes. Humans are capable of navigating such challenging social interaction settings because of their intrinsic knowledg… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.10585v1-abstract-full').style.display = 'inline'; document.getElementById('2211.10585v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.10585v1-abstract-full" style="display: none;"> Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles (HVs) is challenging, as HVs continuously update their policies in response to AVs. In order to navigate safely in the presence of complex AV-HV social interactions, the AVs must learn to predict these changes. Humans are capable of navigating such challenging social interaction settings because of their intrinsic knowledge about other agents behaviors and use that to forecast what might happen in the future. Inspired by humans, we provide our AVs the capability of anticipating future states and leveraging prediction in a cooperative reinforcement learning (RL) decision-making framework, to improve safety and robustness. In this paper, we propose an integration of two essential and earlier-presented components of AVs: social navigation and prediction. We formulate the AV decision-making process as a RL problem and seek to obtain optimal policies that produce socially beneficial results utilizing a prediction-aware planning and social-aware optimization RL framework. We also propose a Hybrid Predictive Network (HPN) that anticipates future observations. The HPN is used in a multi-step prediction chain to compute a window of predicted future observations to be used by the value function network (VFN). Finally, a safe VFN is trained to optimize a social utility using a sequence of previous and predicted observations, and a safety prioritizer is used to leverage the interpretable kinematic predictions to mask the unsafe actions, constraining the RL policy. We compare our prediction-aware AV to state-of-the-art solutions and demonstrate performance improvements in terms of efficiency and safety in multiple simulated scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.10585v1-abstract-full').style.display = 'none'; document.getElementById('2211.10585v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.05540">arXiv:2208.05540</a> <span> [<a href="https://arxiv.org/pdf/2208.05540">pdf</a>, <a href="https://arxiv.org/format/2208.05540">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Augmented Driver Behavior Models for High-Fidelity Simulation Study of Crash Detection Algorithms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jami%2C+A">Ahura Jami</a>, <a href="/search/cs?searchtype=author&query=Razzaghpour%2C+M">Mahdi Razzaghpour</a>, <a href="/search/cs?searchtype=author&query=Alnuweiri%2C+H">Hussein Alnuweiri</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2208.05540v2-abstract-short" style="display: inline;"> Developing safety and efficiency applications for Connected and Automated Vehicles (CAVs) require a great deal of testing and evaluation. The need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly, possibly dangerous, and time-consuming. As an alternative, researchers attempt to study and evaluate their algorithms and designs u… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.05540v2-abstract-full').style.display = 'inline'; document.getElementById('2208.05540v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.05540v2-abstract-full" style="display: none;"> Developing safety and efficiency applications for Connected and Automated Vehicles (CAVs) require a great deal of testing and evaluation. The need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly, possibly dangerous, and time-consuming. As an alternative, researchers attempt to study and evaluate their algorithms and designs using simulation platforms. Modeling the behavior of drivers or human operators in CAVs or other vehicles interacting with them is one of the main challenges of such simulations. While developing a perfect model for human behavior is a challenging task and an open problem, we present a significant augmentation of the current models used in simulators for driver behavior. In this paper, we present a simulation platform for a hybrid transportation system that includes both human-driven and automated vehicles. In addition, we decompose the human driving task and offer a modular approach to simulating a large-scale traffic scenario, allowing for a thorough investigation of automated and active safety systems. Such representation through Interconnected modules offers a human-interpretable system that can be tuned to represent different classes of drivers. Additionally, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of various human-specific and system-specific factors, studying their effect on traffic network performance and safety. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.05540v2-abstract-full').style.display = 'none'; document.getElementById('2208.05540v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.15772">arXiv:2203.15772</a> <span> [<a href="https://arxiv.org/pdf/2203.15772">pdf</a>, <a href="https://arxiv.org/format/2203.15772">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Impact of Information Flow Topology on Safety of Tightly-coupled Connected and Automated Vehicle Platoons Utilizing Stochastic Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Razzaghpour%2C+M">Mahdi Razzaghpour</a>, <a href="/search/cs?searchtype=author&query=Mosharafian%2C+S">Sahand Mosharafian</a>, <a href="/search/cs?searchtype=author&query=Raftari%2C+A">Arash Raftari</a>, <a href="/search/cs?searchtype=author&query=Velni%2C+J+M">Javad Mohammadpour Velni</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.15772v1-abstract-short" style="display: inline;"> Cooperative driving, enabled by Vehicle-to-Everything (V2X) communication, is expected to significantly contribute to the transportation system's safety and efficiency. Cooperative Adaptive Cruise Control (CACC), a major cooperative driving application, has been the subject of many studies in recent years. The primary motivation behind using CACC is to reduce traffic congestion and improve traffic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.15772v1-abstract-full').style.display = 'inline'; document.getElementById('2203.15772v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.15772v1-abstract-full" style="display: none;"> Cooperative driving, enabled by Vehicle-to-Everything (V2X) communication, is expected to significantly contribute to the transportation system's safety and efficiency. Cooperative Adaptive Cruise Control (CACC), a major cooperative driving application, has been the subject of many studies in recent years. The primary motivation behind using CACC is to reduce traffic congestion and improve traffic flow, traffic throughput, and highway capacity. Since the information flow between cooperative vehicles can significantly affect the dynamics of a platoon, the design and performance of control components are tightly dependent on the communication component performance. In addition, the choice of Information Flow Topology (IFT) can affect certain platoons properties such as stability and scalability. Although cooperative vehicles perception can be expanded to multiple predecessors information by using V2X communication, the communication technologies still suffer from scalability issues. Therefore, cooperative vehicles are required to predict each other's behavior to compensate for the effects of non-ideal communication. The notion of Model-Based Communication (MBC) was proposed to enhance cooperative vehicles perception under non-ideal communication by introducing a new flexible content structure for broadcasting joint vehicles dynamic/drivers behavior models. By utilizing a non-parametric (Bayesian) modeling scheme, i.e., Gaussian Process Regression (GPR), and the MBC concept, this paper develops a discrete hybrid stochastic model predictive control approach and examines the impact of communication losses and different information flow topologies on the performance and safety of the platoon. The results demonstrate an improvement in response time and safety using more vehicles information, validating the potential of cooperation to attenuate disturbances and improve traffic flow and safety. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.15772v1-abstract-full').style.display = 'none'; document.getElementById('2203.15772v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.00881">arXiv:2202.00881</a> <span> [<a href="https://arxiv.org/pdf/2202.00881">pdf</a>, <a href="https://arxiv.org/format/2202.00881">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Pedarsani%2C+R">Ramtin Pedarsani</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.00881v1-abstract-short" style="display: inline;"> Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in the real world where they will be surrounded by human-driven vehicles (HVs) is extremely challenging. Prior works have shown the possibilities of creating inter-agent cooperation between a group of AVs that follow a social utility. Such altruistic AVs can form alliances and affect the behavior of HVs to achiev… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.00881v1-abstract-full').style.display = 'inline'; document.getElementById('2202.00881v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.00881v1-abstract-full" style="display: none;"> Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in the real world where they will be surrounded by human-driven vehicles (HVs) is extremely challenging. Prior works have shown the possibilities of creating inter-agent cooperation between a group of AVs that follow a social utility. Such altruistic AVs can form alliances and affect the behavior of HVs to achieve socially desirable outcomes. We identify two major challenges in the co-existence of AVs and HVs. First, social preferences and individual traits of a given human driver, e.g., selflessness and aggressiveness are unknown to an AV, and it is almost impossible to infer them in real-time during a short AV-HV interaction. Second, contrary to AVs that are expected to follow a policy, HVs do not necessarily follow a stationary policy and therefore are extremely hard to predict. To alleviate the above-mentioned challenges, we formulate the mixed-autonomy problem as a multi-agent reinforcement learning (MARL) problem and propose a decentralized framework and reward function for training cooperative AVs. Our approach enables AVs to learn the decision-making of HVs implicitly from experience, optimizes for a social utility while prioritizing safety and allowing adaptability; robustifying altruistic AVs to different human behaviors and constraining them to a safe action space. Finally, we investigate the robustness, safety and sensitivity of AVs to various HVs behavioral traits and present the settings in which the AVs can learn cooperative policies that are adaptable to different situations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.00881v1-abstract-full').style.display = 'none'; document.getElementById('2202.00881v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.07173">arXiv:2111.07173</a> <span> [<a href="https://arxiv.org/pdf/2111.07173">pdf</a>, <a href="https://arxiv.org/format/2111.07173">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Finite State Markov Modeling of C-V2X Erasure Links For Performance and Stability Analysis of Platooning Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Razzaghpour%2C+M">Mahdi Razzaghpour</a>, <a href="/search/cs?searchtype=author&query=Datar%2C+A">Adwait Datar</a>, <a href="/search/cs?searchtype=author&query=Schneider%2C+D">Daniel Schneider</a>, <a href="/search/cs?searchtype=author&query=Zaman%2C+M">Mahdi Zaman</a>, <a href="/search/cs?searchtype=author&query=Werner%2C+H">Herbert Werner</a>, <a href="/search/cs?searchtype=author&query=Frey%2C+H">Hannes Frey</a>, <a href="/search/cs?searchtype=author&query=Velni%2C+J+M">Javad Mohammadpour Velni</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2111.07173v2-abstract-short" style="display: inline;"> Cooperative driving systems, such as platooning, rely on communication and information exchange to create situational awareness for each agent. Design and performance of control components are therefore tightly coupled with communication component performance. The information flow between vehicles can significantly affect the dynamics of a platoon. Therefore, both the performance and the stability… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.07173v2-abstract-full').style.display = 'inline'; document.getElementById('2111.07173v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.07173v2-abstract-full" style="display: none;"> Cooperative driving systems, such as platooning, rely on communication and information exchange to create situational awareness for each agent. Design and performance of control components are therefore tightly coupled with communication component performance. The information flow between vehicles can significantly affect the dynamics of a platoon. Therefore, both the performance and the stability of a platoon depend not only on the vehicle's controller but also on the information flow Topology (IFT). The IFT can cause limitations for certain platoon properties, i.e., stability and scalability. Cellular Vehicle-To-Everything (C-V2X) has emerged as one of the main communication technologies to support connected and automated vehicle applications. As a result of packet loss, wireless channels create random link interruption and changes in network topologies. In this paper, we model the communication links between vehicles with a first-order Markov model to capture the prevalent time correlations for each link. These models enable performance evaluation through better approximation of communication links during system design stages. Our approach is to use data from experiments to model the Inter-Packet Gap (IPG) using Markov chains and derive transition probability matrices for consecutive IPG states. Training data is collected from high fidelity simulations using models derived based on empirical data for a variety of different vehicle densities and communication rates. Utilizing the IPG models, we analyze the mean-square stability of a platoon of vehicles with the standard consensus protocol tuned for ideal communication and compare the degradation in performance for different scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.07173v2-abstract-full').style.display = 'none'; document.getElementById('2111.07173v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.07162">arXiv:2111.07162</a> <span> [<a href="https://arxiv.org/pdf/2111.07162">pdf</a>, <a href="https://arxiv.org/format/2111.07162">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Gaussian Process based Stochastic Model Predictive Control for Cooperative Adaptive Cruise Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mosharafian%2C+S">Sahand Mosharafian</a>, <a href="/search/cs?searchtype=author&query=Razzaghpour%2C+M">Mahdi Razzaghpour</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Velni%2C+J+M">Javad Mohammadpour Velni</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2111.07162v1-abstract-short" style="display: inline;"> Cooperative driving relies on communication among vehicles to create situational awareness. One application of cooperative driving is Cooperative Adaptive Cruise Control (CACC) that aims at enhancing highway transportation safety and capacity. Model-based communication (MBC) is a new paradigm with a flexible content structure for broadcasting joint vehicle-driver predictive behavioral models. The… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.07162v1-abstract-full').style.display = 'inline'; document.getElementById('2111.07162v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.07162v1-abstract-full" style="display: none;"> Cooperative driving relies on communication among vehicles to create situational awareness. One application of cooperative driving is Cooperative Adaptive Cruise Control (CACC) that aims at enhancing highway transportation safety and capacity. Model-based communication (MBC) is a new paradigm with a flexible content structure for broadcasting joint vehicle-driver predictive behavioral models. The vehicle's complex dynamics and diverse driving behaviors add complexity to the modeling process. Gaussian process (GP) is a fully data-driven and non-parametric Bayesian modeling approach which can be used as a modeling component of MBC. The knowledge about the uncertainty is propagated through predictions by generating local GPs for vehicles and broadcasting their hyper-parameters as a model to the neighboring vehicles. In this research study, GP is used to model each vehicle's speed trajectory, which allows vehicles to access the future behavior of their preceding vehicle during communication loss and/or low-rate communication. Besides, to overcome the safety issues in a vehicle platoon, two operating modes for each vehicle are considered; free following and emergency braking. This paper presents a discrete hybrid stochastic model predictive control, which incorporates system modes as well as uncertainties captured by GP models. The proposed control design approach finds the optimal vehicle speed trajectory with the goal of achieving a safe and efficient platoon of vehicles with small inter-vehicle gap while reducing the reliance of the vehicles on a frequent communication. Simulation studies demonstrate the efficacy of the proposed controller considering the aforementioned communication paradigm with low-rate intermittent communication. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.07162v1-abstract-full').style.display = 'none'; document.getElementById('2111.07162v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2021 IEEE Vehicular Networking Conference (VNC) (IEEE VNC 2021) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.03688">arXiv:2111.03688</a> <span> [<a href="https://arxiv.org/pdf/2111.03688">pdf</a>, <a href="https://arxiv.org/format/2111.03688">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Towards Learning Generalizable Driving Policies from Restricted Latent Representations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Pedarsani%2C+R">Ramtin Pedarsani</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2111.03688v2-abstract-short" style="display: inline;"> Training intelligent agents that can drive autonomously in various urban and highway scenarios has been a hot topic in the robotics society within the last decades. However, the diversity of driving environments in terms of road topology and positioning of the neighboring vehicles makes this problem very challenging. It goes without saying that although scenario-specific driving policies for auton… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.03688v2-abstract-full').style.display = 'inline'; document.getElementById('2111.03688v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.03688v2-abstract-full" style="display: none;"> Training intelligent agents that can drive autonomously in various urban and highway scenarios has been a hot topic in the robotics society within the last decades. However, the diversity of driving environments in terms of road topology and positioning of the neighboring vehicles makes this problem very challenging. It goes without saying that although scenario-specific driving policies for autonomous driving are promising and can improve transportation safety and efficiency, they are clearly not a universal scalable solution. Instead, we seek decision-making schemes and driving policies that can generalize to novel and unseen environments. In this work, we capitalize on the key idea that human drivers learn abstract representations of their surroundings that are fairly similar among various driving scenarios and environments. Through these representations, human drivers are able to quickly adapt to novel environments and drive in unseen conditions. Formally, through imposing an information bottleneck, we extract a latent representation that minimizes the \textit{distance} -- a quantification that we introduce to gauge the similarity among different driving configurations -- between driving scenarios. This latent space is then employed as the input to a Q-learning module to learn generalizable driving policies. Our experiments revealed that, using this latent representation can reduce the number of crashes to about half. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.03688v2-abstract-full').style.display = 'none'; document.getElementById('2111.03688v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Under review in an IEEE Journal</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.05664">arXiv:2107.05664</a> <span> [<a href="https://arxiv.org/pdf/2107.05664">pdf</a>, <a href="https://arxiv.org/format/2107.05664">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Sadigh%2C+D">Dorsa Sadigh</a>, <a href="/search/cs?searchtype=author&query=Pedarsani%2C+R">Ramtin Pedarsani</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2107.05664v1-abstract-short" style="display: inline;"> With the adoption of autonomous vehicles on our roads, we will witness a mixed-autonomy environment where autonomous and human-driven vehicles must learn to co-exist by sharing the same road infrastructure. To attain socially-desirable behaviors, autonomous vehicles must be instructed to consider the utility of other vehicles around them in their decision-making process. Particularly, we study the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.05664v1-abstract-full').style.display = 'inline'; document.getElementById('2107.05664v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.05664v1-abstract-full" style="display: none;"> With the adoption of autonomous vehicles on our roads, we will witness a mixed-autonomy environment where autonomous and human-driven vehicles must learn to co-exist by sharing the same road infrastructure. To attain socially-desirable behaviors, autonomous vehicles must be instructed to consider the utility of other vehicles around them in their decision-making process. Particularly, we study the maneuver planning problem for autonomous vehicles and investigate how a decentralized reward structure can induce altruism in their behavior and incentivize them to account for the interest of other autonomous and human-driven vehicles. This is a challenging problem due to the ambiguity of a human driver's willingness to cooperate with an autonomous vehicle. Thus, in contrast with the existing works which rely on behavior models of human drivers, we take an end-to-end approach and let the autonomous agents to implicitly learn the decision-making process of human drivers only from experience. We introduce a multi-agent variant of the synchronous Advantage Actor-Critic (A2C) algorithm and train agents that coordinate with each other and can affect the behavior of human drivers to improve traffic flow and safety. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.05664v1-abstract-full').style.display = 'none'; document.getElementById('2107.05664v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) - Workshop on Autonomous Driving: Perception, Prediction and Planning</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.00898">arXiv:2107.00898</a> <span> [<a href="https://arxiv.org/pdf/2107.00898">pdf</a>, <a href="https://arxiv.org/format/2107.00898">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Cooperative Autonomous Vehicles that Sympathize with Human Drivers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Sadigh%2C+D">Dorsa Sadigh</a>, <a href="/search/cs?searchtype=author&query=Pedarsani%2C+R">Ramtin Pedarsani</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2107.00898v1-abstract-short" style="display: inline;"> Widespread adoption of autonomous vehicles will not become a reality until solutions are developed that enable these intelligent agents to co-exist with humans. This includes safely and efficiently interacting with human-driven vehicles, especially in both conflictive and competitive scenarios. We build up on the prior work on socially-aware navigation and borrow the concept of social value orient… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.00898v1-abstract-full').style.display = 'inline'; document.getElementById('2107.00898v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.00898v1-abstract-full" style="display: none;"> Widespread adoption of autonomous vehicles will not become a reality until solutions are developed that enable these intelligent agents to co-exist with humans. This includes safely and efficiently interacting with human-driven vehicles, especially in both conflictive and competitive scenarios. We build up on the prior work on socially-aware navigation and borrow the concept of social value orientation from psychology -- that formalizes how much importance a person allocates to the welfare of others -- in order to induce altruistic behavior in autonomous driving. In contrast with existing works that explicitly model the behavior of human drivers and rely on their expected response to create opportunities for cooperation, our Sympathetic Cooperative Driving (SymCoDrive) paradigm trains altruistic agents that realize safe and smooth traffic flow in competitive driving scenarios only from experiential learning and without any explicit coordination. We demonstrate a significant improvement in both safety and traffic-level metrics as a result of this altruistic behavior and importantly conclude that the level of altruism in agents requires proper tuning as agents that are too altruistic also lead to sub-optimal traffic flow. The code and supplementary material are available at: https://symcodrive.toghi.net/ <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.00898v1-abstract-full').style.display = 'none'; document.getElementById('2107.00898v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.00200">arXiv:2107.00200</a> <span> [<a href="https://arxiv.org/pdf/2107.00200">pdf</a>, <a href="https://arxiv.org/format/2107.00200">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Social Coordination and Altruism in Autonomous Driving </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Sadigh%2C+D">Dorsa Sadigh</a>, <a href="/search/cs?searchtype=author&query=Pedarsani%2C+R">Ramtin Pedarsani</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2107.00200v4-abstract-short" style="display: inline;"> Despite the advances in the autonomous driving domain, autonomous vehicles (AVs) are still inefficient and limited in terms of cooperating with each other or coordinating with vehicles operated by humans. A group of autonomous and human-driven vehicles (HVs) which work together to optimize an altruistic social utility -- as opposed to the egoistic individual utility -- can co-exist seamlessly and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.00200v4-abstract-full').style.display = 'inline'; document.getElementById('2107.00200v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.00200v4-abstract-full" style="display: none;"> Despite the advances in the autonomous driving domain, autonomous vehicles (AVs) are still inefficient and limited in terms of cooperating with each other or coordinating with vehicles operated by humans. A group of autonomous and human-driven vehicles (HVs) which work together to optimize an altruistic social utility -- as opposed to the egoistic individual utility -- can co-exist seamlessly and assure safety and efficiency on the road. Achieving this mission without explicit coordination among agents is challenging, mainly due to the difficulty of predicting the behavior of humans with heterogeneous preferences in mixed-autonomy environments. Formally, we model an AV's maneuver planning in mixed-autonomy traffic as a partially-observable stochastic game and attempt to derive optimal policies that lead to socially-desirable outcomes using a multi-agent reinforcement learning framework. We introduce a quantitative representation of the AVs' social preferences and design a distributed reward structure that induces altruism into their decision making process. Our altruistic AVs are able to form alliances, guide the traffic, and affect the behavior of the HVs to handle competitive driving scenarios. As a case study, we compare egoistic AVs to our altruistic autonomous agents in a highway merging setting and demonstrate the emerging behaviors that lead to a noticeable improvement in the number of successful merges as well as the overall traffic flow and safety. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.00200v4-abstract-full').style.display = 'none'; document.getElementById('2107.00200v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Under Review in an IEEE Journal</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.06548">arXiv:2101.06548</a> <span> [<a href="https://arxiv.org/pdf/2101.06548">pdf</a>, <a href="https://arxiv.org/ps/2101.06548">ps</a>, <a href="https://arxiv.org/format/2101.06548">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> RVE-CV2X: A Scalable Emulation Framework for Real-Time Evaluation of CV2X-Based Connected Vehicle Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shah%2C+G">Ghayoor Shah</a>, <a href="/search/cs?searchtype=author&query=Saifuddin%2C+M">MD Saifuddin</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+S+D">Somak Datta Gupta</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.06548v1-abstract-short" style="display: inline;"> Vehicle-to-Everything (V2X) communication has become an integral component of Intelligent Transportation Systems (ITS) due to its ability to connect vehicles, pedestrians, infrastructure, and create situational awareness among vehicles. Cellular-Vehicle-to-Everything (C-V2X), based on 3rd Generation Partnership Project (3GPP) Release 14, is one such communication technology that has recently gaine… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.06548v1-abstract-full').style.display = 'inline'; document.getElementById('2101.06548v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.06548v1-abstract-full" style="display: none;"> Vehicle-to-Everything (V2X) communication has become an integral component of Intelligent Transportation Systems (ITS) due to its ability to connect vehicles, pedestrians, infrastructure, and create situational awareness among vehicles. Cellular-Vehicle-to-Everything (C-V2X), based on 3rd Generation Partnership Project (3GPP) Release 14, is one such communication technology that has recently gained significant attention to cater the needs of V2X communication. However, for a successful deployment of C-V2X, it is of paramount significance to thoroughly test the performance of this technology. It is unfeasible to physically conduct a V2X communication experiment to test the performance of C-V2X by arranging hundreds of real vehicles and their transceiving on-board units. Although multiple simulators based on frameworks such as NS-3, OMNET++ and OPNET have proven to be reliable and economic alternatives to using real vehicles, all these simulators are time-consuming and require several orders of magnitudes longer than the actual simulation time. As opposed to physical field- and simulation-based testing, network emulators can provide more realistic and repeatable results for testing vehicular communication. This paper proposes a real-time, high-fidelity, hardware-in-the-loop network emulator (RVE-CV2X) based on C-V2X mode 4 that can provide scalable, reliable and repeatable testing scenarios for V2X communication. The accuracy of this emulator is verified by comparing it to an already validated C-V2X simulator based on the NS-3 framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.06548v1-abstract-full').style.display = 'none'; document.getElementById('2101.06548v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.08317">arXiv:2011.08317</a> <span> [<a href="https://arxiv.org/pdf/2011.08317">pdf</a>, <a href="https://arxiv.org/ps/2011.08317">ps</a>, <a href="https://arxiv.org/format/2011.08317">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Marvasti%2C+E+E">Ehsan Emad Marvasti</a>, <a href="/search/cs?searchtype=author&query=Raftari%2C+A">Arash Raftari</a>, <a href="/search/cs?searchtype=author&query=Marvasti%2C+A+E">Amir Emad Marvasti</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+R">Rui Guo</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+H">Hongsheng Lu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2011.08317v3-abstract-short" style="display: inline;"> The recent advancement in computational and communication systems has led to the introduction of high-performing neural networks and high-speed wireless vehicular communication networks. As a result, new technologies such as cooperative perception and cognition have emerged, addressing the inherent limitations of sensory devices by providing solutions for the detection of partially occluded target… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.08317v3-abstract-full').style.display = 'inline'; document.getElementById('2011.08317v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.08317v3-abstract-full" style="display: none;"> The recent advancement in computational and communication systems has led to the introduction of high-performing neural networks and high-speed wireless vehicular communication networks. As a result, new technologies such as cooperative perception and cognition have emerged, addressing the inherent limitations of sensory devices by providing solutions for the detection of partially occluded targets and expanding the sensing range. However, designing a reliable cooperative cognition or perception system requires addressing the challenges caused by limited network resources and discrepancies between the data shared by different sources. In this paper, we examine the requirements, limitations, and performance of different cooperative perception techniques, and present an in-depth analysis of the notion of Deep Feature Sharing (DFS). We explore different cooperative object detection designs and evaluate their performance in terms of average precision. We use the Volony dataset for our experimental study. The results confirm that the DFS methods are significantly less sensitive to the localization error caused by GPS noise. Furthermore, the results attest that detection gain of DFS methods caused by adding more cooperative participants in the scenes is comparable to raw information sharing technique while DFS enables flexibility in design toward satisfying communication requirements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.08317v3-abstract-full').style.display = 'none'; document.getElementById('2011.08317v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 12 figures, 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.11353">arXiv:2010.11353</a> <span> [<a href="https://arxiv.org/pdf/2010.11353">pdf</a>, <a href="https://arxiv.org/ps/2010.11353">ps</a>, <a href="https://arxiv.org/format/2010.11353">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Marvasti%2C+E+E">Ehsan Emad Marvasti</a>, <a href="/search/cs?searchtype=author&query=Raftari%2C+A">Arash Raftari</a>, <a href="/search/cs?searchtype=author&query=Marvasti%2C+A+E">Amir Emad Marvasti</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2010.11353v1-abstract-short" style="display: inline;"> Situational awareness as a necessity in the connected and autonomous vehicles (CAV) domain is the subject of a significant number of researches in recent years. The driver's safety is directly dependent on the robustness, reliability, and scalability of such systems. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing high speed wireless vehicular networks… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.11353v1-abstract-full').style.display = 'inline'; document.getElementById('2010.11353v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.11353v1-abstract-full" style="display: none;"> Situational awareness as a necessity in the connected and autonomous vehicles (CAV) domain is the subject of a significant number of researches in recent years. The driver's safety is directly dependent on the robustness, reliability, and scalability of such systems. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing high speed wireless vehicular networks. These mechanisms mitigate problems such as occlusion and sensor range limitation. However, the network capacity is a factor determining the maximum amount of information being shared among cooperative entities. The notion of feature sharing, proposed in our previous work, aims to address these challenges by maintaining a balance between computation and communication load. In this work, we propose a mechanism to add flexibility in adapting to communication channel capacity and a novel decentralized shared data alignment method to further improve cooperative object detection performance. The performance of the proposed framework is verified through experiments on Volony dataset. The results confirm that our proposed framework outperforms our previous cooperative object detection method (FS-COD) in terms of average precision. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.11353v1-abstract-full').style.display = 'none'; document.getElementById('2010.11353v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 4 figures, 2 table, 2020 IEEE 3rd Connected and Automated Vehicles Symposium: IEEE CAVS 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.03453">arXiv:2008.03453</a> <span> [<a href="https://arxiv.org/pdf/2008.03453">pdf</a>, <a href="https://arxiv.org/format/2008.03453">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Performance Analysis of Cellular-V2X with Adaptive and Selective Power Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saifuddin%2C+M">Md Saifuddin</a>, <a href="/search/cs?searchtype=author&query=Zaman%2C+M">Mahdi Zaman</a>, <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P Fallah</a>, <a href="/search/cs?searchtype=author&query=Rao%2C+J">Jayanthi Rao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2008.03453v1-abstract-short" style="display: inline;"> LTE based Cellular Vehicle-To-Everything (C-V2X) allows vehicles to communicate with each other directly without the need for infrastructure and is expected to be a critical enabler for connected and autonomous vehicles. V2X communication based safety applications are built on periodic broadcast of basic safety messages with vehicle state information. Vehicles use this information to identify coll… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.03453v1-abstract-full').style.display = 'inline'; document.getElementById('2008.03453v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.03453v1-abstract-full" style="display: none;"> LTE based Cellular Vehicle-To-Everything (C-V2X) allows vehicles to communicate with each other directly without the need for infrastructure and is expected to be a critical enabler for connected and autonomous vehicles. V2X communication based safety applications are built on periodic broadcast of basic safety messages with vehicle state information. Vehicles use this information to identify collision threats and take appropriate countermeasures. As the vehicle density increases, these broadcasts can congest the communication channel resulting in increased packet loss; fundamentally impacting the ability to identify threats in a timely manner. To address this issue, it is important to incorporate a congestion control mechanism. Congestion management scheme based on rate and power control has proved to be effective for DSRC. In this paper, we investigate the suitability of similar congestion control to C-V2X with particular focus on transmit power control. In our evaluation, we include periodic basic safety messages and high priority event messages that are generated when an event such as hard braking occurs. Our study reveals that while power control does not improve packet delivery performance of basic safety messages, it is beneficial to high priority event message delivery. In this paper, we investigate the reasons for this behavior using simulations and analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.03453v1-abstract-full').style.display = 'none'; document.getElementById('2008.03453v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 7 figures, accepted in IEEE CAVS 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.13781">arXiv:2005.13781</a> <span> [<a href="https://arxiv.org/pdf/2005.13781">pdf</a>, <a href="https://arxiv.org/format/2005.13781">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> A Maneuver-based Urban Driving Dataset and Model for Cooperative Vehicle Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Grover%2C+D">Divas Grover</a>, <a href="/search/cs?searchtype=author&query=Razzaghpour%2C+M">Mahdi Razzaghpour</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+R">Rajat Jain</a>, <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Zaman%2C+M">Mahdi Zaman</a>, <a href="/search/cs?searchtype=author&query=Shah%2C+G">Ghayoor Shah</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.13781v4-abstract-short" style="display: inline;"> Short-term future of automated driving can be imagined as a hybrid scenario in which both automated and human-driven vehicles co-exist in the same environment. In order to address the needs of such road configuration, many technology solutions such as vehicular communication and predictive control for automated vehicles have been introduced in the literature. Both aforementioned solutions rely on… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.13781v4-abstract-full').style.display = 'inline'; document.getElementById('2005.13781v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.13781v4-abstract-full" style="display: none;"> Short-term future of automated driving can be imagined as a hybrid scenario in which both automated and human-driven vehicles co-exist in the same environment. In order to address the needs of such road configuration, many technology solutions such as vehicular communication and predictive control for automated vehicles have been introduced in the literature. Both aforementioned solutions rely on driving data of the human driver. In this work, we investigate the currently available driving datasets and introduce a real-world maneuver-based driving dataset that is collected during our urban driving data collection campaign. We also provide a model that embeds the patterns in maneuver-specific samples. Such model can be employed for classification and prediction purposes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.13781v4-abstract-full').style.display = 'none'; document.getElementById('2005.13781v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to IEEE Connected and Automated Vehicle Symposium (IEEE CAVS 2020)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.13837">arXiv:2003.13837</a> <span> [<a href="https://arxiv.org/pdf/2003.13837">pdf</a>, <a href="https://arxiv.org/ps/2003.13837">ps</a>, <a href="https://arxiv.org/format/2003.13837">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Representing Realistic Human Driver Behaviors using a Finite Size Gaussian Process Kernel Bank </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mahjoub%2C+H+N">Hossein Nourkhiz Mahjoub</a>, <a href="/search/cs?searchtype=author&query=Raftari%2C+A">Arash Raftari</a>, <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Mahmud%2C+S+K">Syed K. Mahmud</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.13837v1-abstract-short" style="display: inline;"> The performance of cooperative vehicular applications is tightly dependent on the reliability of the underneath Vehicle-to-Everything (V2X) communication technology. V2X standards, such as Dedicated Short-Range Communications (DSRC) and Cellular-V2X (C-V2X), which are passing their research phase before being mandated in the US, are supposed to serve as reliable circulatory systems for the time-cr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.13837v1-abstract-full').style.display = 'inline'; document.getElementById('2003.13837v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.13837v1-abstract-full" style="display: none;"> The performance of cooperative vehicular applications is tightly dependent on the reliability of the underneath Vehicle-to-Everything (V2X) communication technology. V2X standards, such as Dedicated Short-Range Communications (DSRC) and Cellular-V2X (C-V2X), which are passing their research phase before being mandated in the US, are supposed to serve as reliable circulatory systems for the time-critical information in vehicular networks; however, they are still heavily suffering from scalability issues in real traffic scenarios. The technology-agnostic notion of Model-Based Communications (MBC) has been proposed in our previous works as a promising paradigm to address the scalability issue and its performance, while acquiring different modeling strategies, has been vastly studied. In this work, the modeling capabilities of a powerful non-parametric Bayesian inference scheme, i.e., Gaussian Processes (GPs), is investigated within the MBC context with more details. Our observations reveal an important potential strength of GP-based MBC scheme, i.e., its capability of accurately modeling different driving behavioral patterns by utilizing only a limited size GP kernel bank. This interesting aspect of integrating GP inference with MBC framework, which has been verified in this work using realistic driving data sets, introduces this architecture as a strong and appealing candidate to address the scalability challenge. The results confirm that our proposed approach over-performs the state of the art research in terms of the required communication rate and GP kernel bank size. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.13837v1-abstract-full').style.display = 'none'; document.getElementById('2003.13837v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in IEEE VNC 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.08440">arXiv:2002.08440</a> <span> [<a href="https://arxiv.org/pdf/2002.08440">pdf</a>, <a href="https://arxiv.org/format/2002.08440">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Marvasti%2C+E+E">Ehsan Emad Marvasti</a>, <a href="/search/cs?searchtype=author&query=Raftari%2C+A">Arash Raftari</a>, <a href="/search/cs?searchtype=author&query=Marvasti%2C+A+E">Amir Emad Marvasti</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+R">Rui Guo</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+H">HongSheng Lu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.08440v1-abstract-short" style="display: inline;"> The recent advancements in communication and computational systems has led to significant improvement of situational awareness in connected and autonomous vehicles. Computationally efficient neural networks and high speed wireless vehicular networks have been some of the main contributors to this improvement. However, scalability and reliability issues caused by inherent limitations of sensory and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.08440v1-abstract-full').style.display = 'inline'; document.getElementById('2002.08440v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.08440v1-abstract-full" style="display: none;"> The recent advancements in communication and computational systems has led to significant improvement of situational awareness in connected and autonomous vehicles. Computationally efficient neural networks and high speed wireless vehicular networks have been some of the main contributors to this improvement. However, scalability and reliability issues caused by inherent limitations of sensory and communication systems are still challenging problems. In this paper, we aim to mitigate the effects of these limitations by introducing the concept of feature sharing for cooperative object detection (FS-COD). In our proposed approach, a better understanding of the environment is achieved by sharing partially processed data between cooperative vehicles while maintaining a balance between computation and communication load. This approach is different from current methods of map sharing, or sharing of raw data which are not scalable. The performance of the proposed approach is verified through experiments on Volony dataset. It is shown that the proposed approach has significant performance superiority over the conventional single-vehicle object detection approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.08440v1-abstract-full').style.display = 'none'; document.getElementById('2002.08440v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1905.09267">arXiv:1905.09267</a> <span> [<a href="https://arxiv.org/pdf/1905.09267">pdf</a>, <a href="https://arxiv.org/ps/1905.09267">ps</a>, <a href="https://arxiv.org/format/1905.09267">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Real-Time Hardware-In-the-Loop Emulation Framework for DSRC-based Connected Vehicle Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shah%2C+G">Ghayoor Shah</a>, <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+N">Nitish Gupta</a>, <a href="/search/cs?searchtype=author&query=Gani%2C+S+M+O">S M Osman Gani</a>, <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+S+D">Somak Datta Gupta</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1905.09267v2-abstract-short" style="display: inline;"> The rapid growth of connected and automated vehicle (CAV) solutions have made a significant impact on the safety of intelligent transportation systems. However, similar to any other emerging technology, thorough testing and evaluation studies are of paramount importance for the effectiveness of these solutions. Due to the safety-critical nature of this problem, large-scale real-world field tests d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.09267v2-abstract-full').style.display = 'inline'; document.getElementById('1905.09267v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1905.09267v2-abstract-full" style="display: none;"> The rapid growth of connected and automated vehicle (CAV) solutions have made a significant impact on the safety of intelligent transportation systems. However, similar to any other emerging technology, thorough testing and evaluation studies are of paramount importance for the effectiveness of these solutions. Due to the safety-critical nature of this problem, large-scale real-world field tests do not seem to be a feasible and practical option. Thus, employing simulation and emulation approaches are preferred in the development phase of the safety-related applications in CAVs. Such methodologies not only mitigate the high cost of deploying large number of real vehicles, but also enable researchers to exhaustively perform repeatable tests in various scenarios. Software simulation of very large-scale vehicular scenarios is mostly a time consuming task and as a matter of fact, any simulation environment would include abstractions in order to model the real-world system. In contrast to the simulation-based solutions, network emulators are able to produce more realistic test environments. In this work, we propose a high-fidelity hardware-in-the-loop network emulator framework in order to create testing environments for vehicle-to-vehicle (V2V) communication. The proposed architecture is able to run in real-time fashion in contrast to other existing systems, which can potentially boost the development and validation of V2V systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1905.09267v2-abstract-full').style.display = 'none'; document.getElementById('1905.09267v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.04375">arXiv:1904.04375</a> <span> [<a href="https://arxiv.org/pdf/1904.04375">pdf</a>, <a href="https://arxiv.org/ps/1904.04375">ps</a>, <a href="https://arxiv.org/format/1904.04375">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/IVS.2019.8814260">10.1109/IVS.2019.8814260 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Valiente%2C+R">Rodolfo Valiente</a>, <a href="/search/cs?searchtype=author&query=Zaman%2C+M">Mahdi Zaman</a>, <a href="/search/cs?searchtype=author&query=Ozer%2C+S">Sedat Ozer</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.04375v3-abstract-short" style="display: inline;"> A fundamental challenge in autonomous vehicles is adjusting the steering angle at different road conditions. Recent state-of-the-art solutions addressing this challenge include deep learning techniques as they provide end-to-end solution to predict steering angles directly from the raw input images with higher accuracy. Most of these works ignore the temporal dependencies between the image frames.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.04375v3-abstract-full').style.display = 'inline'; document.getElementById('1904.04375v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.04375v3-abstract-full" style="display: none;"> A fundamental challenge in autonomous vehicles is adjusting the steering angle at different road conditions. Recent state-of-the-art solutions addressing this challenge include deep learning techniques as they provide end-to-end solution to predict steering angles directly from the raw input images with higher accuracy. Most of these works ignore the temporal dependencies between the image frames. In this paper, we tackle the problem of utilizing multiple sets of images shared between two autonomous vehicles to improve the accuracy of controlling the steering angle by considering the temporal dependencies between the image frames. This problem has not been studied in the literature widely. We present and study a new deep architecture to predict the steering angle automatically by using Long-Short-Term-Memory (LSTM) in our deep architecture. Our deep architecture is an end-to-end network that utilizes CNN, LSTM and fully connected (FC) layers and it uses both present and futures images (shared by a vehicle ahead via Vehicle-to-Vehicle (V2V) communication) as input to control the steering angle. Our model demonstrates the lowest error when compared to the other existing approaches in the literature. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.04375v3-abstract-full').style.display = 'none'; document.getElementById('1904.04375v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 May, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 April, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in IV 2019, 6 pages, 9 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1904.00071">arXiv:1904.00071</a> <span> [<a href="https://arxiv.org/pdf/1904.00071">pdf</a>, <a href="https://arxiv.org/ps/1904.00071">ps</a>, <a href="https://arxiv.org/format/1904.00071">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Analysis of Distributed Congestion Control in Cellular Vehicle-to-everything Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Saifuddin%2C+M">Md Saifuddin</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Mughal%2C+M+O">M. O. Mughal</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1904.00071v2-abstract-short" style="display: inline;"> Cellular Vehicle-to-everything (C-V2X) communication has been proposed in the 3rd Generation Partnership Project release 14 standard to address the latency and reliability requirements of cooperative safety applications. Such applications can involve highly congested vehicular scenarios where the network experiences high data loads. Thus, a sophisticated congestion control solution is vital in ord… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.00071v2-abstract-full').style.display = 'inline'; document.getElementById('1904.00071v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1904.00071v2-abstract-full" style="display: none;"> Cellular Vehicle-to-everything (C-V2X) communication has been proposed in the 3rd Generation Partnership Project release 14 standard to address the latency and reliability requirements of cooperative safety applications. Such applications can involve highly congested vehicular scenarios where the network experiences high data loads. Thus, a sophisticated congestion control solution is vital in order to maintain the network performance required for safety-related applications. With the aid of our high-fidelity link-level network simulator, we investigate the feasibility of implementing the distributed congestion control algorithm specified in SAE J2945/1 standard on top of the C-V2X stack. We describe our implementation and evaluate the performance of transmission rate and range control mechanisms using relevant metrics. Additionally, we identify areas for potential design enhancements and further investigation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1904.00071v2-abstract-full').style.display = 'none'; document.getElementById('1904.00071v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted in IEEE Vehicular Technology Conference (IEEE VTC-fall2019)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1903.01576">arXiv:1903.01576</a> <span> [<a href="https://arxiv.org/pdf/1903.01576">pdf</a>, <a href="https://arxiv.org/ps/1903.01576">ps</a>, <a href="https://arxiv.org/format/1903.01576">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mahjoub%2C+H+N">Hossein Nourkhiz Mahjoub</a>, <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Gani%2C+S+M+O">S M Osman Gani</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1903.01576v2-abstract-short" style="display: inline;"> Scalable communication is of utmost importance for reliable dissemination of time-sensitive information in cooperative vehicular ad-hoc networks (VANETs), which is, in turn, an essential prerequisite for the proper operation of the critical cooperative safety applications. The model-based communication (MBC) is a recently-explored scalability solution proposed in the literature, which has shown a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.01576v2-abstract-full').style.display = 'inline'; document.getElementById('1903.01576v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1903.01576v2-abstract-full" style="display: none;"> Scalable communication is of utmost importance for reliable dissemination of time-sensitive information in cooperative vehicular ad-hoc networks (VANETs), which is, in turn, an essential prerequisite for the proper operation of the critical cooperative safety applications. The model-based communication (MBC) is a recently-explored scalability solution proposed in the literature, which has shown a promising potential to reduce the channel congestion to a great extent. In this work, based on the MBC notion, a technology-agnostic hybrid model selection policy for Vehicle-to-Everything (V2X) communication is proposed which benefits from the characteristics of the non-parametric Bayesian inference techniques, specifically Gaussian Processes. The results show the effectiveness of the proposed communication architecture on both reducing the required message exchange rate and increasing the remote agent tracking precision. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1903.01576v2-abstract-full').style.display = 'none'; document.getElementById('1903.01576v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 March, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for Oral Presentation at the 13th IEEE Systems Conference (SysCon 2019)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1809.07928">arXiv:1809.07928</a> <span> [<a href="https://arxiv.org/pdf/1809.07928">pdf</a>, <a href="https://arxiv.org/format/1809.07928">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3392058">10.1145/3392058 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Prospect Theoretic Approach for Trust Management in IoT Networks under Manipulation Attacks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Salimitari%2C+M">Mehrdad Salimitari</a>, <a href="/search/cs?searchtype=author&query=Bhattacharjee%2C+S">Shameek Bhattacharjee</a>, <a href="/search/cs?searchtype=author&query=Chatterjee%2C+M">Mainak Chatterjee</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1809.07928v4-abstract-short" style="display: inline;"> As Internet of Things (IoT) and Cyber-Physical systems become more ubiquitous and an integral part of our daily lives, it is important that we are able to trust the data aggregate from such systems. However, the interpretation of trustworthiness is contextual and varies according to the risk tolerance attitude of the concerned application and varying levels of uncertainty associated with the evide… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.07928v4-abstract-full').style.display = 'inline'; document.getElementById('1809.07928v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1809.07928v4-abstract-full" style="display: none;"> As Internet of Things (IoT) and Cyber-Physical systems become more ubiquitous and an integral part of our daily lives, it is important that we are able to trust the data aggregate from such systems. However, the interpretation of trustworthiness is contextual and varies according to the risk tolerance attitude of the concerned application and varying levels of uncertainty associated with the evidence upon which trust models act. Hence, the data integrity scoring mechanisms should have provisions to adapt to varying risk attitudes and uncertainties. In this paper, we propose a Bayesian inference model and a prospect theoretic framework for data integrity scoring that quantify the trustworthiness of data collected from IoT devices in the presence of an adversaries who manipulate the data. We consider an imperfect anomaly monitoring mechanism that monitors the data being sent from each device and classifies the outcome as not compromised, compromised, and cannot be inferred. These outcomes are conceptualized as a multinomial hypothesis of a Bayesian inference model with three parameters which are then used for calculating a utility value on how reliable the aggregate data is. We use a prospect theory inspired approach to quantify this data integrity score and evaluate the trustworthiness of the aggregate data from the IoT framework. Furthermore, we also model the system using the traditionally used expected utility theory and compare the results with that obtained using prospect theory. As decisions are based on how the data is fused, we propose two measuring models: one optimistic and another conservative. The proposed framework is validated using extensive simulation experiments. We show how data integrity scores vary under a variety of system factors like attack intensity and inaccurate detection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.07928v4-abstract-full').style.display = 'none'; document.getElementById('1809.07928v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 September, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper is accepted for publication in ACM Transactions on Sensor Networks (TOSN)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> ACM Trans. Sen. Netw. 16, 3, Article 26 (May 2020) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1809.02678">arXiv:1809.02678</a> <span> [<a href="https://arxiv.org/pdf/1809.02678">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Multiple Access in Cellular V2X: Performance Analysis in Highly Congested Vehicular Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Toghi%2C+B">Behrad Toghi</a>, <a href="/search/cs?searchtype=author&query=Saifuddin%2C+M">Md Saifuddin</a>, <a href="/search/cs?searchtype=author&query=Mahjoub%2C+H+N">Hossein Nourkhiz Mahjoub</a>, <a href="/search/cs?searchtype=author&query=Mughal%2C+M+O">M. O. Mughal</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Rao%2C+J">Jayanthi Rao</a>, <a href="/search/cs?searchtype=author&query=Das%2C+S">Sushanta Das</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1809.02678v2-abstract-short" style="display: inline;"> Vehicle-to-everything (V2X) communication enables vehicles, roadside vulnerable users, and infrastructure facilities to communicate in an ad-hoc fashion. Cellular V2X (C-V2X), which was introduced in the 3rd generation partnership project (3GPP) release 14 standard, has recently received significant attention due to its perceived ability to address the scalability and reliability requirements of v… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.02678v2-abstract-full').style.display = 'inline'; document.getElementById('1809.02678v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1809.02678v2-abstract-full" style="display: none;"> Vehicle-to-everything (V2X) communication enables vehicles, roadside vulnerable users, and infrastructure facilities to communicate in an ad-hoc fashion. Cellular V2X (C-V2X), which was introduced in the 3rd generation partnership project (3GPP) release 14 standard, has recently received significant attention due to its perceived ability to address the scalability and reliability requirements of vehicular safety applications. In this paper, we provide a comprehensive study of the resource allocation of the C-V2X multiple access mechanism for high-density vehicular networks, as it can strongly impact the key performance indicators such as latency and packet delivery rate. Phenomena that can affect the communication performance are investigated and a detailed analysis of the cases that can cause possible performance degradation or system limitations, is provided. The results indicate that a unified system configuration may be necessary for all vehicles, as it is mandated for IEEE 802.11p, in order to obtain the optimum performance. In the end, we show the inter-dependence of different parameters on the resource allocation procedure with the aid of our high fidelity simulator. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1809.02678v2-abstract-full').style.display = 'none'; document.getElementById('1809.02678v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 September, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to the IEEE Vehicular Networking Conference (VNC 2018), Taipei, Taiwan</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.06463">arXiv:1808.06463</a> <span> [<a href="https://arxiv.org/pdf/1808.06463">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/MITS.2017.2743201">10.1109/MITS.2017.2743201 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tahmasbi-Sarvestani%2C+A">Amin Tahmasbi-Sarvestani</a>, <a href="/search/cs?searchtype=author&query=Mahjoub%2C+H+N">Hossein Nourkhiz Mahjoub</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Moradi-Pari%2C+E">Ehsan Moradi-Pari</a>, <a href="/search/cs?searchtype=author&query=Abuchaar%2C+O">Oubada Abuchaar</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1808.06463v1-abstract-short" style="display: inline;"> While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.06463v1-abstract-full').style.display = 'inline'; document.getElementById('1808.06463v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.06463v1-abstract-full" style="display: none;"> While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this perspective. Leveraging the existing V2V platforms, we propose a new framework using a DSRC-enabled smartphone to extend safety benefits to VRUs. The interoperability of applications between vehicles and portable DSRC enabled devices is achieved through the SAE J2735 Personal Safety Message (PSM). However, considering the fact that VRU movement dynamics, response times, and crash scenarios are fundamentally different from vehicles, a specific framework should be designed for VRU safety applications to study their performance. In this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios. The details of our VRU safety module, including target classification and collision detection algorithms, are explained next. Furthermore, we propose and evaluate a mitigating solution for congestion and power consumption issues in such systems. Finally, the whole system is implemented and analyzed for realistic crash scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.06463v1-abstract-full').style.display = 'none'; document.getElementById('1808.06463v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Intelligent Transportation Systems Magazine, vol. 9, no. 4, pp. 62-75, winter 2017 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.00516">arXiv:1808.00516</a> <span> [<a href="https://arxiv.org/pdf/1808.00516">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.39">10.1109/DASC-PICom-DataCom-CyberSciTec.2017.39 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mahjoub%2C+H+N">Hossein Nourkhiz Mahjoub</a>, <a href="/search/cs?searchtype=author&query=Tahmasbi-Sarvestani%2C+A">Amin Tahmasbi-Sarvestani</a>, <a href="/search/cs?searchtype=author&query=Kazemi%2C+H">Hadi Kazemi</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1808.00516v1-abstract-short" style="display: inline;"> Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory predic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.00516v1-abstract-full').style.display = 'inline'; document.getElementById('1808.00516v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.00516v1-abstract-full" style="display: none;"> Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory prediction problem needs to be deeply investigated in order to come up with an end to end framework with enough precision required by the safety applications' controllers. This problem has been tackled in the literature using different methods. However, machine learning, which is a promising and emerging field with remarkable potential for time series prediction, has not been explored enough for this purpose. In this paper, a two-layer neural network-based system is developed which predicts the future values of vehicle parameters, such as velocity, acceleration, and yaw rate, in the first layer and then predicts the two-dimensional, i.e. longitudinal and lateral, trajectory points based on the first layer's outputs. The performance of the proposed framework has been evaluated in realistic cut-in scenarios from Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable improvement in the prediction accuracy in comparison with the kinematics model which is the dominant employed model by the automotive industry. Both ideal and nonideal communication circumstances have been investigated for our system evaluation. For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.00516v1-abstract-full').style.display = 'none'; document.getElementById('1808.00516v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2018. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2017 IEEE Cyber Science and Technology Congress(CyberSciTech), Orlando, FL, 2017, pp. 156-163 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1409.4470">arXiv:1409.4470</a> <span> [<a href="https://arxiv.org/pdf/1409.4470">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Adaptive Content Control for Communication amongst Cooperative Automated Vehicles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fanaei%2C+M">Mohammad Fanaei</a>, <a href="/search/cs?searchtype=author&query=Tahmasbi-Sarvestani%2C+A">Amin Tahmasbi-Sarvestani</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Bansal%2C+G">Gaurav Bansal</a>, <a href="/search/cs?searchtype=author&query=Valenti%2C+M+C">Matthew C. Valenti</a>, <a href="/search/cs?searchtype=author&query=Kenney%2C+J+B">John B. Kenney</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1409.4470v1-abstract-short" style="display: inline;"> Cooperative automated vehicles exchange information to assist each other in creating a more precise and extended view of their surroundings, with the aim of improving automated-driving decisions. This paper addresses the need for scalable communication among these vehicles. To this end, a general communication framework is proposed through which automated cars exchange information derived from mul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1409.4470v1-abstract-full').style.display = 'inline'; document.getElementById('1409.4470v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1409.4470v1-abstract-full" style="display: none;"> Cooperative automated vehicles exchange information to assist each other in creating a more precise and extended view of their surroundings, with the aim of improving automated-driving decisions. This paper addresses the need for scalable communication among these vehicles. To this end, a general communication framework is proposed through which automated cars exchange information derived from multi-resolution maps created using their local sensing modalities. This method can extend the region visible to a car beyond the area directly sensed by its own sensors. An adaptive, probabilistic, distance-dependent strategy is proposed that controls the content of the messages exchanged among vehicles based on performance measures associated with the load on the communication channel. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1409.4470v1-abstract-full').style.display = 'none'; document.getElementById('1409.4470v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 September, 2014; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2014. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 Pages, 10 Figures, Sixth International Symposium on Wireless Vehicular Communications (WiVEC'2014)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1206.0323">arXiv:1206.0323</a> <span> [<a href="https://arxiv.org/pdf/1206.0323">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Fairness and Stability Analysis of Congestion Control Schemes in Vehicular Ad-hoc Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Nasiriani%2C+N">Neda Nasiriani</a>, <a href="/search/cs?searchtype=author&query=Fallah%2C+Y+P">Yaser P. Fallah</a>, <a href="/search/cs?searchtype=author&query=Krishnan%2C+H">Hariharan Krishnan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1206.0323v1-abstract-short" style="display: inline;"> Cooperative vehicle safety (CVS) systems operate based on broadcast of vehicle position and safety information to neighboring cars. The communication medium of CVS is a vehicular ad-hoc network. One of the main challenges in large scale deployment of CVS systems is the issue of scalability. To address the scalability problem, several congestion control methods have been proposed and are currently… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1206.0323v1-abstract-full').style.display = 'inline'; document.getElementById('1206.0323v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1206.0323v1-abstract-full" style="display: none;"> Cooperative vehicle safety (CVS) systems operate based on broadcast of vehicle position and safety information to neighboring cars. The communication medium of CVS is a vehicular ad-hoc network. One of the main challenges in large scale deployment of CVS systems is the issue of scalability. To address the scalability problem, several congestion control methods have been proposed and are currently under field study. These algorithms adapt transmission rate and power based on network measures such as channel busy ratio. We examine two such algorithms and study their dynamic behavior in time and space to evaluate stability (in time) and fairness (in space) properties of these algorithms. We present stability conditions and evaluate stability and fairness of the algorithms through simulation experiments. Results show that there is a trade-off between fast convergence, temporal stability and spatial fairness. The proper ranges of parameters for achieving stability are presented for the discussed algorithms. Stability is verified for all typical road density cases. Fairness is shown to be naturally achieved for some algorithms, while under the same conditions other algorithms may suffer from unfairness issues. A method for resolving unfairness is introduced and evaluated through simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1206.0323v1-abstract-full').style.display = 'none'; document.getElementById('1206.0323v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 June, 2012; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2012. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a 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