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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/2110.09741">arXiv:2110.09741</a> <span> [<a href="https://arxiv.org/pdf/2110.09741">pdf</a>, <a href="https://arxiv.org/format/2110.09741">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</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"> Trajectory Prediction with Linguistic Representations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kuo%2C+Y">Yen-Ling Kuo</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+X">Xin Huang</a>, <a href="/search/cs?searchtype=author&query=Barbu%2C+A">Andrei Barbu</a>, <a href="/search/cs?searchtype=author&query=McGill%2C+S+G">Stephen G. McGill</a>, <a href="/search/cs?searchtype=author&query=Katz%2C+B">Boris Katz</a>, <a href="/search/cs?searchtype=author&query=Leonard%2C+J+J">John J. Leonard</a>, <a href="/search/cs?searchtype=author&query=Rosman%2C+G">Guy Rosman</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="2110.09741v2-abstract-short" style="display: inline;"> Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory prediction model that uses linguistic intermediate representations to forecast trajectories, and is trained using trajectory samples with partially-annotated captions. The model learns the meaning of each of the words without dir… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.09741v2-abstract-full').style.display = 'inline'; document.getElementById('2110.09741v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.09741v2-abstract-full" style="display: none;"> Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory prediction model that uses linguistic intermediate representations to forecast trajectories, and is trained using trajectory samples with partially-annotated captions. The model learns the meaning of each of the words without direct per-word supervision. At inference time, it generates a linguistic description of trajectories which captures maneuvers and interactions over an extended time interval. This generated description is used to refine predictions of the trajectories of multiple agents. We train and validate our model on the Argoverse dataset, and demonstrate improved accuracy results in trajectory prediction. In addition, our model is more interpretable: it presents part of its reasoning in plain language as captions, which can aid model development and can aid in building confidence in the model before deploying it. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.09741v2-abstract-full').style.display = 'none'; document.getElementById('2110.09741v2-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> 9 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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 ICRA 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/2110.08750">arXiv:2110.08750</a> <span> [<a href="https://arxiv.org/pdf/2110.08750">pdf</a>, <a href="https://arxiv.org/format/2110.08750">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> <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"> TIP: Task-Informed Motion Prediction for Intelligent Vehicles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huang%2C+X">Xin Huang</a>, <a href="/search/cs?searchtype=author&query=Rosman%2C+G">Guy Rosman</a>, <a href="/search/cs?searchtype=author&query=Jasour%2C+A">Ashkan Jasour</a>, <a href="/search/cs?searchtype=author&query=McGill%2C+S+G">Stephen G. McGill</a>, <a href="/search/cs?searchtype=author&query=Leonard%2C+J+J">John J. Leonard</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+B+C">Brian C. Williams</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="2110.08750v2-abstract-short" style="display: inline;"> When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples. This constraint requires the predictors to generate samples that best support the task given task specifications. However, existing predictors are often optimized and evaluated via task-agnostic measures without accounting for the use of predictions in downstre… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.08750v2-abstract-full').style.display = 'inline'; document.getElementById('2110.08750v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.08750v2-abstract-full" style="display: none;"> When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples. This constraint requires the predictors to generate samples that best support the task given task specifications. However, existing predictors are often optimized and evaluated via task-agnostic measures without accounting for the use of predictions in downstream tasks, and thus could result in sub-optimal task performance. In this paper, we propose a task-informed motion prediction model that better supports the tasks through its predictions, by jointly reasoning about prediction accuracy and the utility of the downstream tasks, which is commonly used to evaluate the task performance. The task utility function does not require the full task information, but rather a specification of the utility of the task, resulting in predictors that serve a wide range of downstream tasks. We demonstrate our approach on two use cases of common decision making tasks and their utility functions, in the context of autonomous driving and parallel autonomy. Experiment results show that our predictor produces accurate predictions that improve the task performance by a large margin in both tasks when compared to task-agnostic baselines on the Waymo Open Motion dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.08750v2-abstract-full').style.display = 'none'; document.getElementById('2110.08750v2-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, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">9 pages, 5 figures, 5 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.02344">arXiv:2110.02344</a> <span> [<a href="https://arxiv.org/pdf/2110.02344">pdf</a>, <a href="https://arxiv.org/format/2110.02344">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> <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"> HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huang%2C+X">Xin Huang</a>, <a href="/search/cs?searchtype=author&query=Rosman%2C+G">Guy Rosman</a>, <a href="/search/cs?searchtype=author&query=Gilitschenski%2C+I">Igor Gilitschenski</a>, <a href="/search/cs?searchtype=author&query=Jasour%2C+A">Ashkan Jasour</a>, <a href="/search/cs?searchtype=author&query=McGill%2C+S+G">Stephen G. McGill</a>, <a href="/search/cs?searchtype=author&query=Leonard%2C+J+J">John J. Leonard</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+B+C">Brian C. Williams</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="2110.02344v1-abstract-short" style="display: inline;"> Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicting continuous trajectories, to improve accuracy and support explainability. However, these approaches often assume the intent to remain fixed over the prediction horizon, which is problematic in practice, especially over… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.02344v1-abstract-full').style.display = 'inline'; document.getElementById('2110.02344v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.02344v1-abstract-full" style="display: none;"> Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicting continuous trajectories, to improve accuracy and support explainability. However, these approaches often assume the intent to remain fixed over the prediction horizon, which is problematic in practice, especially over longer horizons. To overcome this limitation, we introduce HYPER, a general and expressive hybrid prediction framework that models evolving human intent. By modeling traffic agents as a hybrid discrete-continuous system, our approach is capable of predicting discrete intent changes over time. We learn the probabilistic hybrid model via a maximum likelihood estimation problem and leverage neural proposal distributions to sample adaptively from the exponentially growing discrete space. The overall approach affords a better trade-off between accuracy and coverage. We train and validate our model on the Argoverse dataset, and demonstrate its effectiveness through comprehensive ablation studies and comparisons with state-of-the-art models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.02344v1-abstract-full').style.display = 'none'; document.getElementById('2110.02344v1-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 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">12 pages, 10 figures, 4 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.08003">arXiv:2003.08003</a> <span> [<a href="https://arxiv.org/pdf/2003.08003">pdf</a>, <a href="https://arxiv.org/format/2003.08003">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> <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"> CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huang%2C+X">Xin Huang</a>, <a href="/search/cs?searchtype=author&query=McGill%2C+S+G">Stephen G. McGill</a>, <a href="/search/cs?searchtype=author&query=DeCastro%2C+J+A">Jonathan A. DeCastro</a>, <a href="/search/cs?searchtype=author&query=Fletcher%2C+L">Luke Fletcher</a>, <a href="/search/cs?searchtype=author&query=Leonard%2C+J+J">John J. Leonard</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+B+C">Brian C. Williams</a>, <a href="/search/cs?searchtype=author&query=Rosman%2C+G">Guy Rosman</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="2003.08003v2-abstract-short" style="display: inline;"> Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems. Traditional confidence measures on predictions often ignore the way predicted trajectories affect downstream decisions for safe driving. In this paper, we propose a novel multi-task intent recognition neural network that predicts not only probabilistic driver trajectories, but also utility statisti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.08003v2-abstract-full').style.display = 'inline'; document.getElementById('2003.08003v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.08003v2-abstract-full" style="display: none;"> Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems. Traditional confidence measures on predictions often ignore the way predicted trajectories affect downstream decisions for safe driving. In this paper, we propose a novel multi-task intent recognition neural network that predicts not only probabilistic driver trajectories, but also utility statistics associated with the predictions for a given downstream task. We establish a decision criterion for parallel autonomy that takes into account the role of driver trajectory prediction in real-time decision making by reasoning about estimated task-specific utility statistics. We further improve the robustness of our system by considering uncertainties in downstream planning tasks that may lead to unsafe decisions. We test our online system on a realistic urban driving dataset, and demonstrate its advantage in terms of recall and fall-out metrics compared to baseline methods, and demonstrate its effectiveness in intervention and warning use cases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.08003v2-abstract-full').style.display = 'none'; document.getElementById('2003.08003v2-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 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 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 at ICRA'21/RA-L'21. Author version with 9 pages, 5 figures, 2 algorithms</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.12736">arXiv:1911.12736</a> <span> [<a href="https://arxiv.org/pdf/1911.12736">pdf</a>, <a href="https://arxiv.org/format/1911.12736">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> <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"> DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huang%2C+X">Xin Huang</a>, <a href="/search/cs?searchtype=author&query=McGill%2C+S+G">Stephen G. McGill</a>, <a href="/search/cs?searchtype=author&query=DeCastro%2C+J+A">Jonathan A. DeCastro</a>, <a href="/search/cs?searchtype=author&query=Fletcher%2C+L">Luke Fletcher</a>, <a href="/search/cs?searchtype=author&query=Leonard%2C+J+J">John J. Leonard</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+B+C">Brian C. Williams</a>, <a href="/search/cs?searchtype=author&query=Rosman%2C+G">Guy Rosman</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="1911.12736v2-abstract-short" style="display: inline;"> Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it -- a key ability for evaluating safety from a planning and verification perspective. In this work, we devise a novel approach for generating realistic and diverse vehicl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.12736v2-abstract-full').style.display = 'inline'; document.getElementById('1911.12736v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.12736v2-abstract-full" style="display: none;"> Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it -- a key ability for evaluating safety from a planning and verification perspective. In this work, we devise a novel approach for generating realistic and diverse vehicle trajectories. We extend the generative adversarial network (GAN) framework with a low-dimensional approximate semantic space, and shape that space to capture semantics such as merging and turning. We sample from this space in a way that mimics the predicted distribution, but allows us to control coverage of semantically distinct outcomes. We validate our approach on a publicly available dataset and show results that achieve state-of-the-art prediction performance, while providing improved coverage of the space of predicted trajectory semantics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.12736v2-abstract-full').style.display = 'none'; document.getElementById('1911.12736v2-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 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">8 pages, 5 figures, 1 table</span> </p> </li> </ol> <div class="is-hidden-tablet">  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