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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/2411.08999">arXiv:2411.08999</a> <span> [<a href="https://arxiv.org/pdf/2411.08999">pdf</a>, <a href="https://arxiv.org/format/2411.08999">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="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Learning-Based Control Barrier Function with Provably Safe Guarantees: Reducing Conservatism with Heading-Aware Safety Margin </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jianye Xu</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</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="2411.08999v1-abstract-short" style="display: inline;"> We propose a learning-based Control Barrier Function (CBF) to reduce conservatism in collision avoidance of car-like robots. Traditional CBFs often use Euclidean distance between robots' centers as safety margin, neglecting headings and simplifying geometries to circles. While this ensures smooth, differentiable safety functions required by CBFs, it can be overly conservative in tight environments… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08999v1-abstract-full').style.display = 'inline'; document.getElementById('2411.08999v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08999v1-abstract-full" style="display: none;"> We propose a learning-based Control Barrier Function (CBF) to reduce conservatism in collision avoidance of car-like robots. Traditional CBFs often use Euclidean distance between robots' centers as safety margin, neglecting headings and simplifying geometries to circles. While this ensures smooth, differentiable safety functions required by CBFs, it can be overly conservative in tight environments. To address this limitation, we design a heading-aware safety margin that accounts for the robots' orientations, enabling a less conservative and more accurate estimation of safe regions. Since the function computing this safety margin is non-differentiable, we approximate it with a neural network to ensure differentiability and facilitate integration with CBFs. We describe how we achieve bounded learning error and incorporate the upper bound into the CBF to provide formal safety guarantees through forward invariance. We show that our CBF is a high-order CBF with relative degree two for a system with two robots whose dynamics are modeled by the nonlinear kinematic bicycle model. Experimental results in overtaking and bypassing scenarios reveal a 33.5 % reduction in conservatism compared to traditional methods, while maintaining safety. Code: https://github.com/bassamlab/sigmarl <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08999v1-abstract-full').style.display = 'none'; document.getElementById('2411.08999v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </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, 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/2409.11852">arXiv:2409.11852</a> <span> [<a href="https://arxiv.org/pdf/2409.11852">pdf</a>, <a href="https://arxiv.org/format/2409.11852">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="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> XP-MARL: Auxiliary Prioritization in Multi-Agent Reinforcement Learning to Address Non-Stationarity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jianye Xu</a>, <a href="/search/cs?searchtype=author&query=Sobhy%2C+O">Omar Sobhy</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</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="2409.11852v1-abstract-short" style="display: inline;"> Non-stationarity poses a fundamental challenge in Multi-Agent Reinforcement Learning (MARL), arising from agents simultaneously learning and altering their policies. This creates a non-stationary environment from the perspective of each individual agent, often leading to suboptimal or even unconverged learning outcomes. We propose an open-source framework named XP-MARL, which augments MARL with au… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11852v1-abstract-full').style.display = 'inline'; document.getElementById('2409.11852v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.11852v1-abstract-full" style="display: none;"> Non-stationarity poses a fundamental challenge in Multi-Agent Reinforcement Learning (MARL), arising from agents simultaneously learning and altering their policies. This creates a non-stationary environment from the perspective of each individual agent, often leading to suboptimal or even unconverged learning outcomes. We propose an open-source framework named XP-MARL, which augments MARL with auxiliary prioritization to address this challenge in cooperative settings. XP-MARL is 1) founded upon our hypothesis that prioritizing agents and letting higher-priority agents establish their actions first would stabilize the learning process and thus mitigate non-stationarity and 2) enabled by our proposed mechanism called action propagation, where higher-priority agents act first and communicate their actions, providing a more stationary environment for others. Moreover, instead of using a predefined or heuristic priority assignment, XP-MARL learns priority-assignment policies with an auxiliary MARL problem, leading to a joint learning scheme. Experiments in a motion-planning scenario involving Connected and Automated Vehicles (CAVs) demonstrate that XP-MARL improves the safety of a baseline model by 84.4% and outperforms a state-of-the-art approach, which improves the baseline by only 12.8%. Code: github.com/cas-lab-munich/sigmarl <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11852v1-abstract-full').style.display = 'none'; document.getElementById('2409.11852v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </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, 5 figures. This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.10215">arXiv:2409.10215</a> <span> [<a href="https://arxiv.org/pdf/2409.10215">pdf</a>, <a href="https://arxiv.org/format/2409.10215">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="Multiagent Systems">cs.MA</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"> Synchronization-Based Cooperative Distributed Model Predictive Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Beerwerth%2C+J">Julius Beerwerth</a>, <a href="/search/cs?searchtype=author&query=Kloock%2C+M">Maximilian Kloock</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</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="2409.10215v1-abstract-short" style="display: inline;"> Distributed control algorithms are known to reduce overall computation time compared to centralized control algorithms. However, they can result in inconsistent solutions leading to the violation of safety-critical constraints. Inconsistent solutions can arise when two or more agents compute concurrently while making predictions on each others control actions. To address this issue, we propose an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10215v1-abstract-full').style.display = 'inline'; document.getElementById('2409.10215v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10215v1-abstract-full" style="display: none;"> Distributed control algorithms are known to reduce overall computation time compared to centralized control algorithms. However, they can result in inconsistent solutions leading to the violation of safety-critical constraints. Inconsistent solutions can arise when two or more agents compute concurrently while making predictions on each others control actions. To address this issue, we propose an iterative algorithm called Synchronization-Based Cooperative Distributed Model Predictive Control, which we presented in [1]. The algorithm consists of two steps: 1. computing the optimal control inputs for each agent and 2. synchronizing the predicted states across all agents. We demonstrate the efficacy of our algorithm in the control of multiple small-scale vehicles in our Cyber-Physical Mobility Lab. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10215v1-abstract-full').style.display = 'none'; document.getElementById('2409.10215v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </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 work was submitted to the Symposium on Systems Theory in Data and Optimization as an extended abstract</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.05029">arXiv:2409.05029</a> <span> [<a href="https://arxiv.org/pdf/2409.05029">pdf</a>, <a href="https://arxiv.org/format/2409.05029">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="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</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/10.23919/ECC64448.2024.10591179">10.23919/ECC64448.2024.10591179 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Limiting Computation Levels in Prioritized Trajectory Planning with Safety Guarantees </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jianye Xu</a>, <a href="/search/cs?searchtype=author&query=Scheffe%2C+P">Patrick Scheffe</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</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="2409.05029v1-abstract-short" style="display: inline;"> In prioritized planning for vehicles, vehicles plan trajectories in parallel or in sequence. Parallel prioritized planning offers approximately consistent computation time regardless of the number of vehicles but struggles to guarantee collision-free trajectories. Conversely, sequential prioritized planning can guarantee collision-freeness but results in increased computation time as the number of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05029v1-abstract-full').style.display = 'inline'; document.getElementById('2409.05029v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.05029v1-abstract-full" style="display: none;"> In prioritized planning for vehicles, vehicles plan trajectories in parallel or in sequence. Parallel prioritized planning offers approximately consistent computation time regardless of the number of vehicles but struggles to guarantee collision-free trajectories. Conversely, sequential prioritized planning can guarantee collision-freeness but results in increased computation time as the number of sequentially computing vehicles, which we term computation levels, grows. This number is determined by the directed coupling graph resulted from the coupling and prioritization of vehicles. In this work, we guarantee safe trajectories in parallel planning through reachability analysis. Although these trajectories are collision-free, they tend to be conservative. We address this by planning with a subset of vehicles in sequence. We formulate the problem of selecting this subset as a graph partitioning problem that allows us to independently set computation levels. Our simulations demonstrate a reduction in computation levels by approximately 64% compared to sequential prioritized planning while maintaining the solution quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05029v1-abstract-full').style.display = 'none'; document.getElementById('2409.05029v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </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. This is an extended abstract of our previous work published at the 2024 European Control Conference (ECC), June 25-28, 2024. Stockholm, Sweden</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.14199">arXiv:2408.14199</a> <span> [<a href="https://arxiv.org/pdf/2408.14199">pdf</a>, <a href="https://arxiv.org/format/2408.14199">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="Multiagent Systems">cs.MA</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/10.13140/RG.2.2.16176.74248/1">10.13140/RG.2.2.16176.74248/1 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Survey on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mokhtarian%2C+A">Armin Mokhtarian</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jianye Xu</a>, <a href="/search/cs?searchtype=author&query=Scheffe%2C+P">Patrick Scheffe</a>, <a href="/search/cs?searchtype=author&query=Kloock%2C+M">Maximilian Kloock</a>, <a href="/search/cs?searchtype=author&query=Sch%C3%A4fer%2C+S">Simon Sch盲fer</a>, <a href="/search/cs?searchtype=author&query=Bang%2C+H">Heeseung Bang</a>, <a href="/search/cs?searchtype=author&query=Le%2C+V">Viet-Anh Le</a>, <a href="/search/cs?searchtype=author&query=Ulhas%2C+S">Sangeet Ulhas</a>, <a href="/search/cs?searchtype=author&query=Betz%2C+J">Johannes Betz</a>, <a href="/search/cs?searchtype=author&query=Wilson%2C+S">Sean Wilson</a>, <a href="/search/cs?searchtype=author&query=Berman%2C+S">Spring Berman</a>, <a href="/search/cs?searchtype=author&query=Paull%2C+L">Liam Paull</a>, <a href="/search/cs?searchtype=author&query=Prorok%2C+A">Amanda Prorok</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</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="2408.14199v2-abstract-short" style="display: inline;"> Connected and automated vehicles and robot swarms hold transformative potential for enhancing safety, efficiency, and sustainability in the transportation and manufacturing sectors. Extensive testing and validation of these technologies is crucial for their deployment in the real world. While simulations are essential for initial testing, they often have limitations in capturing the complex dynami… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.14199v2-abstract-full').style.display = 'inline'; document.getElementById('2408.14199v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.14199v2-abstract-full" style="display: none;"> Connected and automated vehicles and robot swarms hold transformative potential for enhancing safety, efficiency, and sustainability in the transportation and manufacturing sectors. Extensive testing and validation of these technologies is crucial for their deployment in the real world. While simulations are essential for initial testing, they often have limitations in capturing the complex dynamics of real-world interactions. This limitation underscores the importance of small-scale testbeds. These testbeds provide a realistic, cost-effective, and controlled environment for testing and validating algorithms, acting as an essential intermediary between simulation and full-scale experiments. This work serves to facilitate researchers' efforts in identifying existing small-scale testbeds suitable for their experiments and provide insights for those who want to build their own. In addition, it delivers a comprehensive survey of the current landscape of these testbeds. We derive 62 characteristics of testbeds based on the well-known sense-plan-act paradigm and offer an online table comparing 23 small-scale testbeds based on these characteristics. The online table is hosted on our designated public webpage https://bassamlab.github.io/testbeds-survey, and we invite testbed creators and developers to contribute to it. We closely examine nine testbeds in this paper, demonstrating how the derived characteristics can be used to present testbeds. Furthermore, we discuss three ongoing challenges concerning small-scale testbeds that we identified, i.e., small-scale to full-scale transition, sustainability, and power and resource management. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.14199v2-abstract-full').style.display = 'none'; document.getElementById('2408.14199v2-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </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">16 pages, 11 figures, 1 table. This work was accepted by the IEEE Robotics & Automation Magazine</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.07644">arXiv:2408.07644</a> <span> [<a href="https://arxiv.org/pdf/2408.07644">pdf</a>, <a href="https://arxiv.org/format/2408.07644">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</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/10.13140/RG.2.2.24505.17769">10.13140/RG.2.2.24505.17769 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jianye Xu</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+P">Pan Hu</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</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="2408.07644v1-abstract-short" style="display: inline;"> This paper introduces an open-source, decentralized framework named SigmaRL, designed to enhance both sample efficiency and generalization of multi-agent Reinforcement Learning (RL) for motion planning of connected and automated vehicles. Most RL agents exhibit a limited capacity to generalize, often focusing narrowly on specific scenarios, and are usually evaluated in similar or even the same sce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.07644v1-abstract-full').style.display = 'inline'; document.getElementById('2408.07644v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.07644v1-abstract-full" style="display: none;"> This paper introduces an open-source, decentralized framework named SigmaRL, designed to enhance both sample efficiency and generalization of multi-agent Reinforcement Learning (RL) for motion planning of connected and automated vehicles. Most RL agents exhibit a limited capacity to generalize, often focusing narrowly on specific scenarios, and are usually evaluated in similar or even the same scenarios seen during training. Various methods have been proposed to address these challenges, including experience replay and regularization. However, how observation design in RL affects sample efficiency and generalization remains an under-explored area. We address this gap by proposing five strategies to design information-dense observations, focusing on general features that are applicable to most traffic scenarios. We train our RL agents using these strategies on an intersection and evaluate their generalization through numerical experiments across completely unseen traffic scenarios, including a new intersection, an on-ramp, and a roundabout. Incorporating these information-dense observations reduces training times to under one hour on a single CPU, and the evaluation results reveal that our RL agents can effectively zero-shot generalize. Code: github.com/cas-lab-munich/SigmaRL <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.07644v1-abstract-full').style.display = 'none'; document.getElementById('2408.07644v1-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> 14 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </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, 5 figures, accepted for presentation at the IEEE International Conference on Intelligent Transportation Systems (ITSC) 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.14276">arXiv:2401.14276</a> <span> [<a href="https://arxiv.org/pdf/2401.14276">pdf</a>, <a href="https://arxiv.org/format/2401.14276">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 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/10.1515/auto-2022-0158">10.1515/auto-2022-0158 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Optimization-based motion primitive automata for autonomous driving </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pedrosa%2C+M+V+A">Matheus V. A. Pedrosa</a>, <a href="/search/cs?searchtype=author&query=Scheffe%2C+P">Patrick Scheffe</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</a>, <a href="/search/cs?searchtype=author&query=Fla%C3%9Fkamp%2C+K">Kathrin Fla脽kamp</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="2401.14276v1-abstract-short" style="display: inline;"> Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typically, multiple criteria have to be taken into account, multiobjective optimization problems have to be solved. For the resulting Pareto-optimal motion primitives, we introduce a unive… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.14276v1-abstract-full').style.display = 'inline'; document.getElementById('2401.14276v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.14276v1-abstract-full" style="display: none;"> Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typically, multiple criteria have to be taken into account, multiobjective optimization problems have to be solved. For the resulting Pareto-optimal motion primitives, we introduce a universal automaton, which can be reduced or reconfigured according to prioritized criteria during planning. We evaluate a corresponding multi-vehicle planning scenario with both simulations and laboratory experiments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.14276v1-abstract-full').style.display = 'none'; document.getElementById('2401.14276v1-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> 25 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.09335">arXiv:2104.09335</a> <span> [<a href="https://arxiv.org/pdf/2104.09335">pdf</a>, <a href="https://arxiv.org/format/2104.09335">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="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Investigating Outdoor Recognition Performance of Infrared Beacons for Infrastructure-based Localization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kampmann%2C+A">Alexandru Kampmann</a>, <a href="/search/cs?searchtype=author&query=Lamberti%2C+M">Michael Lamberti</a>, <a href="/search/cs?searchtype=author&query=Petrovic%2C+N">Nikola Petrovic</a>, <a href="/search/cs?searchtype=author&query=Kowalewski%2C+S">Stefan Kowalewski</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</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="2104.09335v2-abstract-short" style="display: inline;"> This paper demonstrates a system comprised of infrared beacons and a camera equipped with an optical band-pass filter. Our system can reliably detect and identify individual beacons at 100m distance regardless of lighting conditions. We describe the camera and beacon design as well as the image processing pipeline in detail. In our experiments, we investigate and demonstrate the ability of the sys… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.09335v2-abstract-full').style.display = 'inline'; document.getElementById('2104.09335v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.09335v2-abstract-full" style="display: none;"> This paper demonstrates a system comprised of infrared beacons and a camera equipped with an optical band-pass filter. Our system can reliably detect and identify individual beacons at 100m distance regardless of lighting conditions. We describe the camera and beacon design as well as the image processing pipeline in detail. In our experiments, we investigate and demonstrate the ability of the system to recognize our beacons in both daytime and nighttime conditions. High precision localization is a key enabler for automated vehicles but remains unsolved, despite strong recent improvements. Our low-cost, infrastructure-based approach is a potential step towards solving the localization problem. All datasets are made available here https://embedded.rwth-aachen.de/doku.php?id=forschung:mobility:infralocalization:concept. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.09335v2-abstract-full').style.display = 'none'; document.getElementById('2104.09335v2-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 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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 at IEEE Intelligent Vehicle 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.02298">arXiv:2005.02298</a> <span> [<a href="https://arxiv.org/pdf/2005.02298">pdf</a>, <a href="https://arxiv.org/format/2005.02298">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="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Reducing Uncertainty by Fusing Dynamic Occupancy Grid Maps in a Cloud-based Collective Environment Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lampe%2C+B">Bastian Lampe</a>, <a href="/search/cs?searchtype=author&query=van+Kempen%2C+R">Raphael van Kempen</a>, <a href="/search/cs?searchtype=author&query=Woopen%2C+T">Timo Woopen</a>, <a href="/search/cs?searchtype=author&query=Kampmann%2C+A">Alexandru Kampmann</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</a>, <a href="/search/cs?searchtype=author&query=Eckstein%2C+L">Lutz Eckstein</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="2005.02298v1-abstract-short" style="display: inline;"> Accurate environment perception is essential for automated vehicles. Since occlusions and inaccuracies regularly occur, the exchange and combination of perception data of multiple vehicles seems promising. This paper describes a method to combine perception data of automated and connected vehicles in the form of evidential Dynamic Occupany Grid Maps (DOGMas) in a cloud-based system. This system is… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.02298v1-abstract-full').style.display = 'inline'; document.getElementById('2005.02298v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.02298v1-abstract-full" style="display: none;"> Accurate environment perception is essential for automated vehicles. Since occlusions and inaccuracies regularly occur, the exchange and combination of perception data of multiple vehicles seems promising. This paper describes a method to combine perception data of automated and connected vehicles in the form of evidential Dynamic Occupany Grid Maps (DOGMas) in a cloud-based system. This system is called the Collective Environment Model and is part of the cloud system developed in the project UNICARagil. The presented concept extends existing approaches that fuse evidential grid maps representing static environments of a single vehicle to evidential grid maps computed by multiple vehicles in dynamic environments. The developed fusion process additionally incorporates self-reported data provided by connected vehicles instead of only relying on perception data. We show that the uncertainty in a DOGMa described by Shannon entropy as well as the uncertainty described by a non-specificity measure can be reduced. This enables automated and connected vehicles to behave in ways not before possible due to unknown but relevant information about the environment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.02298v1-abstract-full').style.display = 'none'; document.getElementById('2005.02298v1-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 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 be published in 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA, October 20-23, 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/2004.10063">arXiv:2004.10063</a> <span> [<a href="https://arxiv.org/pdf/2004.10063">pdf</a>, <a href="https://arxiv.org/format/2004.10063">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="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Cyber-Physical Mobility Lab: An Open-Source Platform for Networked and Autonomous Vehicles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kloock%2C+M">Maximilian Kloock</a>, <a href="/search/cs?searchtype=author&query=Scheffe%2C+P">Patrick Scheffe</a>, <a href="/search/cs?searchtype=author&query=Maczijewski%2C+J">Janis Maczijewski</a>, <a href="/search/cs?searchtype=author&query=Kampmann%2C+A">Alexandru Kampmann</a>, <a href="/search/cs?searchtype=author&query=Mokhtarian%2C+A">Armin Mokhtarian</a>, <a href="/search/cs?searchtype=author&query=Kowalewski%2C+S">Stefan Kowalewski</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</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="2004.10063v4-abstract-short" style="display: inline;"> This paper introduces our Cyber-Physical Mobility Lab (CPM Lab). It is an open-source development environment for networked and autonomous vehicles with focus on networked decision-making, trajectory planning, and control. The CPM Lab hosts 20 physical model-scale vehicles (渭Cars) which we can seamlessly extend by unlimited simulated vehicles. The code and construction plans are publicly available… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.10063v4-abstract-full').style.display = 'inline'; document.getElementById('2004.10063v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.10063v4-abstract-full" style="display: none;"> This paper introduces our Cyber-Physical Mobility Lab (CPM Lab). It is an open-source development environment for networked and autonomous vehicles with focus on networked decision-making, trajectory planning, and control. The CPM Lab hosts 20 physical model-scale vehicles (渭Cars) which we can seamlessly extend by unlimited simulated vehicles. The code and construction plans are publicly available to enable rebuilding the CPM Lab. Our four-layered architecture enables the seamless use of the same software in simulations and in experiments without any further adaptions. A Data Distribution Service (DDS) based middleware allows adapting the number of vehicles during experiments in a seamless manner. The middleware is also responsible for synchronizing all entities following a logical execution time approach to achieve determinism and reproducibility of experiments. This approach makes the CPM Lab a unique platform for rapid functional prototyping of networked decision-making algorithms. The CPM Lab allows researchers as well as students from different disciplines to see their ideas developing into reality. We demonstrate its capabilities using two example experiments. We are working on a remote access to the CPM Lab via a webinterface. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.10063v4-abstract-full').style.display = 'none'; document.getElementById('2004.10063v4-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 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 April, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">This work has been presented on ECC21</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2004.08364">arXiv:2004.08364</a> <span> [<a href="https://arxiv.org/pdf/2004.08364">pdf</a>, <a href="https://arxiv.org/format/2004.08364">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="Multiagent Systems">cs.MA</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/10.1016/j.ifacol.2020.12.1821">10.1016/j.ifacol.2020.12.1821 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Networked and Autonomous Model-scale Vehicles for Experiments in Research and Education </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Scheffe%2C+P">Patrick Scheffe</a>, <a href="/search/cs?searchtype=author&query=Maczijewski%2C+J">Janis Maczijewski</a>, <a href="/search/cs?searchtype=author&query=Kloock%2C+M">Maximilian Kloock</a>, <a href="/search/cs?searchtype=author&query=Kampmann%2C+A">Alexandru Kampmann</a>, <a href="/search/cs?searchtype=author&query=Derks%2C+A">Andreas Derks</a>, <a href="/search/cs?searchtype=author&query=Kowalewski%2C+S">Stefan Kowalewski</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</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="2004.08364v1-abstract-short" style="display: inline;"> This paper presents the $\mathrm渭$Car, a 1:18 model-scale vehicle with Ackermann steering geometry developed for experiments in networked and autonomous driving in research and education. The vehicle is open source, moderately costed and highly flexible, which allows for many applications. It is equipped with an inertial measurement unit and an odometer and obtains its pose via WLAN from an indoor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.08364v1-abstract-full').style.display = 'inline'; document.getElementById('2004.08364v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2004.08364v1-abstract-full" style="display: none;"> This paper presents the $\mathrm渭$Car, a 1:18 model-scale vehicle with Ackermann steering geometry developed for experiments in networked and autonomous driving in research and education. The vehicle is open source, moderately costed and highly flexible, which allows for many applications. It is equipped with an inertial measurement unit and an odometer and obtains its pose via WLAN from an indoor positioning system. The two supported operating modes for controlling the vehicle are (1) computing control inputs on external hardware, transmitting them via WLAN and applying received inputs to the actuators and (2) transmitting a reference trajectory via WLAN, which is then followed by a controller running on the onboard Raspberry Pi Zero W. The design allows identical vehicles to be used at the same time in order to conduct experiments with a large amount of networked agents. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2004.08364v1-abstract-full').style.display = 'none'; document.getElementById('2004.08364v1-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IFAC-PapersOnLine Volume 53, Issue 2, 2020, Pages 17332-17337 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.05755">arXiv:2002.05755</a> <span> [<a href="https://arxiv.org/pdf/2002.05755">pdf</a>, <a href="https://arxiv.org/format/2002.05755">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"> Vision-Based Real-Time Indoor Positioning System for Multiple Vehicles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kloock%2C+M">Maximilian Kloock</a>, <a href="/search/cs?searchtype=author&query=Scheffe%2C+P">Patrick Scheffe</a>, <a href="/search/cs?searchtype=author&query=T%C3%BClleners%2C+I">Isabelle T眉lleners</a>, <a href="/search/cs?searchtype=author&query=Maczijewski%2C+J">Janis Maczijewski</a>, <a href="/search/cs?searchtype=author&query=Kowalewski%2C+S">Stefan Kowalewski</a>, <a href="/search/cs?searchtype=author&query=Alrifaee%2C+B">Bassam Alrifaee</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="2002.05755v2-abstract-short" style="display: inline;"> We propose a novel external indoor positioning system that computes the position and orientation of multiple model-scale vehicles. For this purpose, we use a camera mounted at a height of 3.3m and LEDs attached to each vehicle. We reach an accuracy of about 1.1 cm for the position and around 0.6 掳 for the orientation in the mean. Our system is real-time capable with a soft deadline of 20 ms. Moreo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.05755v2-abstract-full').style.display = 'inline'; document.getElementById('2002.05755v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.05755v2-abstract-full" style="display: none;"> We propose a novel external indoor positioning system that computes the position and orientation of multiple model-scale vehicles. For this purpose, we use a camera mounted at a height of 3.3m and LEDs attached to each vehicle. We reach an accuracy of about 1.1 cm for the position and around 0.6 掳 for the orientation in the mean. Our system is real-time capable with a soft deadline of 20 ms. Moreover, it is robust against changing lighting conditions and reflections. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.05755v2-abstract-full').style.display = 'none'; document.getElementById('2002.05755v2-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 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 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">Accepted in IFAC Wolrd Congress 2020</span> </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 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