@inproceedings{, author = {Bernhard, Julian and Hart, Patrick and Sahu, Amit and Sch{\"{o}}ller, Christoph and Guzman Cancimance, Michell}, title = {Risk-Based Safety Envelopes for Autonomous Vehicles Under Perception Uncertainty}, booktitle = {Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, year = {2022}, month = jun, address = {Aachen, Germany}, abstract = {Ensuring the safety of autonomous vehicles, given the uncertainty in sensing other road users, is an open problem. Moreover, separate safety specifications for perception and planning components raise how to assess the overall system safety. This work provides a probabilistic approach to calculate safety envelopes under perception uncertainty. The probabilistic envelope definition is based on a risk threshold. It limits the cumulative probability that the actual safety envelope in a fully observable environment is larger than an applied envelope and is solved using iterative worst-case analysis of envelopes. Our approach extends non-probabilistic envelopes - in this work, the Responsibility-Sensitive Safety (RSS) - to handle uncertainties. To evaluate our probabilistic envelope approach, we compare it in a simulated highway merging scenario against several baseline safety architectures. Our evaluation shows that our model allows adjusting safety and performance based on a chosen risk level and the amount of perception uncertainty. We conclude with an outline of how to formally argue safety under perception uncertainty using our formulation of envelope violation risk.}, } @inproceedings{, author = {Bernhard, Julian and Esterle, Klemens and Hart, Patrick and Kessler, Tobias}, title = {BARK: Open Behavior Benchmarking in Multi-Agent Environments}, booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2020}, month = oct, address = {Las Vegas, NV, USA}, abstract = {Predicting and planning interactive behaviors in complex traffic situations presents a challenging task. Especially in scenarios involving multiple traffic participants that interact densely, autonomous vehicles still struggle to interpret situations and to eventually achieve their own mission goal. As driving tests are costly and challenging scenarios are hard to find and reproduce, simulation is widely used to develop, test, and benchmark behavior models. However, most simulations rely on datasets and simplistic behavior models for traffic participants and do not cover the full variety of real-world, interactive human behaviors. In this work, we introduce BARK, an open-source behavior benchmarking environment designed to mitigate the shortcomings stated above. In BARK, behavior models are (re-)used for planning, prediction, and simulation. A range of models is currently available, such as Monte-Carlo Tree Search and Reinforcement Learning-based behavior models. We use a public dataset and sampling-based scenario generation to show the inter-exchangeability of behavior models in BARK. We evaluate how well the models used cope with interactions and how robust they are towards exchanging behavior models. Our evaluation shows that BARK provides a suitable framework for a systematic development of behavior models.}, } @inproceedings{, author = {Hart, Patrick and Knoll, Alois}, title = {Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments}, booktitle = {In Proceedings of the 31st IEEE Intelligent Vehicles Symposium (IV)}, year = {2020}, month = oct, abstract = {Most reinforcement learning approaches used in behavior generation utilize vectorial information as input. However, this requires the network to have a pre-defined input-size -- in semantic environments this means assuming the maximum number of vehicles. Additionally, this vectorial representation is not invariant to the order and number of vehicles. To mitigate the above-stated disadvantages, we propose combining graph neural networks with actor-critic reinforcement learning. As graph neural networks apply the same network to every vehicle and aggregate incoming edge information, they are invariant to the number and order of vehicles. This makes them ideal candidates to be used as networks in semantic environments -- environments consisting of objects lists. Graph neural networks exhibit some other advantages that make them favorable to be used in semantic environments. The relational information is explicitly given and does not have to be inferred. Moreover, graph neural networks propagate information through the network and can gather higher-degree information. We demonstrate our approach using a highway lane-change scenario and compare the performance of graph neural networks to conventional ones. We show that graph neural networks are capable of handling scenarios with a varying number and order of vehicles during training and application.}, } @inproceedings{, author = {Hart, Patrick and Knoll, Alois and Rychly, Leonard}, title = {Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization}, booktitle = {2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, year = {2019}, month = nov, abstract = {Many current behavior generation methods struggle to handle real-world traffic situations as they do not scale well with complexity. However, behaviors can be learned off-line using data-driven approaches. Especially, reinforcement learning is promising as it implicitly learns how to behave utilizing collected experiences. In this work, we combine policy-based reinforcement learning with local optimization to foster and synthesize the best of the two methodologies. The policy-based reinforcement learning algorithm provides an initial solution and guiding reference for the post-optimization. Therefore, the optimizer only has to compute a single homotopy class, e.g. drive behind or in front of the other vehicle. By storing the state-history during reinforcement learning, it can be used for constraint checking and the optimizer can account for interactions. The post-optimization additionally acts as a safety-layer and the novel method, thus, can be applied in safety-critical applications. We evaluate the proposed method using lane-change scenarios with a varying number of vehicles.}, } @proceedings{kessler2019a, author = {Kessler, Tobias and Bernhard, Julian and Buechel, Martin and Esterle, Klemens and Hart, Patrick and Malovetz, Daniel and Truong Le, Michael and Diehl, Frederik and Brunner, Thomas and Knoll, Alois}, title = {Bridging the Gap between Open Source Software and Vehicle Hardware for Autonomous Driving}, booktitle = {2019 IEEE Intelligent Vehicles Symposium}, pages = {1612-1619}, year = {2019}, month = jun, doi = {10.1109/IVS.2019.8813784}, url = {https://doi.org/10.1109/IVS.2019.8813784}, } @conference{, author = {Esterle, Klemens and Hart, Patrick and Bernhard, Julian and Knoll, Alois}, title = {Spatiotemporal Motion Planning with Combinatorial Reasoning for Autonomous Driving}, booktitle = {21st International Conference on Intelligent Transportation Systems, ITSC 2018, Maui, HI, USA, November 4-7, 2018}, pages = {1053-1060}, year = {2018}, month = nov, doi = {10.1109/ITSC.2018.8570003}, url = {https://doi.org/10.1109/ITSC.2018.8570003}, }