@article{, author = {Kessler, Tobias and Esterle, Klemens and Knoll, Alois}, title = {Mixed-Integer Motion Planning on German Roads within the Apollo Driving Stack}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, journal = {IEEE Transactions on Intelligent Vehicles}, year = {2022}, month = mar, doi = {doi:10.1109/tiv.2022.3162671}, } @inproceedings{, author = {Kessler, Tobias and Esterle, Klemens and Knoll, Alois}, title = {Linear Differential Games for Cooperative Behavior Planning of Autonomous Vehicles Using Mixed-Integer Programming}, booktitle = {Proceedings of 59th IEEE Conference on Decision and Control (CDC)}, year = {2020}, month = dec, address = {Seogwipo, South Korea}, abstract = {Cooperatively planning for multiple agents has been proposed as a promising method for strategic and motion planning for automated vehicles. By taking into account the intent of every agent, the ego agent can incorporate future interactions with human-driven vehicles into its planning. The problem is often formulated as a multi-agent game and solved using iterative algorithms operating on a discretized action or state space. Even if converging to a Nash equilibrium, the result will often be only sub-optimal. In this paper, we define a linear differential game for a set of interacting agents and solve it to optimality using mixed-integer programming. A disjunctive formulation of the orientation allows us to formulate linear constraints to prevent agent-to-agent collision while preserving the non-holonomic motion properties of the vehicle model. Soft constraints account for prediction errors. We then define a joint cost function, where a cooperation factor can adapt between altruistic, cooperative, and egoistic behavior. We study the influence of the cooperation factor to solve scenarios, where interaction between the agents is necessary to solve them successfully. The approach is then evaluated in a racing scenario, where we show the applicability of the formulation in a closed-loop receding horizon replanning fashion. By accounting for inaccuracies in the cooperative assumption and the actual behavior, we can indeed successfully plan an optimal control strategy interacting closely with other agents.}, } @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 = {Esterle, Klemens and Kessler, Tobias and Knoll, Alois}, title = {Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation}, booktitle = {Proceedings of the 31st IEEE Intelligent Vehicles Symposium (IV)}, year = {2020}, month = oct, address = {Las Vegas, NV, USA}, abstract = {Mixed-Integer Quadratic Programming (MIQP) has been identified as a suitable approach for finding an optimal solution to the behavior planning problem with low runtimes. Logical constraints and continuous equations are optimized alongside. However, it has only been formulated for a straight road, omitting common situations such as taking turns at intersections. This has prevented the model from being used in reality so far. Based on a triple integrator model formulation, we compute the orientation of the vehicle and model it in a disjunctive manner. That allows us to formulate linear constraints to account for the non-holonomy and collision avoidance. These constraints are approximations, for which we introduce the theory. We show the applicability in two benchmark scenarios and prove the feasibility by solving the same models using nonlinear optimization. This new model will allow researchers to leverage the benefits of MIQP, such as logical constraints, or global optimality.}, } @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}, } @article{, author = {Kessler, Tobias and Knoll, Alois}, title = {Cooperative Multi-Vehicle Behavior Coordination for Autonomous Driving}, journal = {IEEE Intelligent Vehicles Symposium (IV)}, year = {2019}, } @proceedings{, author = {Kessler, Tobias and Minnerup, Pascal and Esterle, Klemens and Feist, Christian and Mickler, Florian and Roth, Erwin and Knoll, Alois}, title = {Roadgraph Generation and Free-Space Estimation in Unknown Structured Environments for Autonomous Vehicle Motion Planning}, booktitle = {21st International Conference on Intelligent Transportation Systems ITSC 2018}, year = {2018}, month = nov, doi = {10.1109/ITSC.2018.8569306}, url = {https://doi.org/10.1109/ITSC.2018.8569306}, } @inproceedings{Kessler2017a, author = {Kessler, Tobias and Knoll, Alois}, title = {Multi vehicle trajectory coordination for automated parking}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, pages = {661 - 666}, year = {2017}, url = {http://ieeexplore.ieee.org/document/7995793/}, } @inproceedings{Kessler2017, author = {Kessler, Tobias and Minnerup, Pascal and Lenz, David and Knoll, Alois}, title = {Systematically comparing control approaches in the presence of actuator errors}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, pages = {353 - 358}, year = {2017}, url = {http://ieeexplore.ieee.org/document/7995744/}, } @inproceedings{Lenz2016a, author = {Lenz, David and Kessler, Tobias and Knoll, Alois}, title = {{Tactical Cooperative Planning for Autonomous Vehicles using MCTS}}, booktitle = {IEEE Intelligent Vehicles Symposium}, year = {2016}, abstract = {Abstract—Human drivers use nonverbal communication and anticipation of other drivers' actions to master conflicts oc- curring in everyday driving situations. Without a high pen- etration of vehicle-to-vehicle communication an autonomous vehicle has to have the possibility to understand intentions of others and share own intentions with the surrounding traffic participants. This paper proposes a cooperative combinatorial motion planning algorithm without the need for inter vehicle communication based on Monte Carlo Tree Search (MCTS).We motivate why MCTS is particularly suited for the autonomous driving domain. Furthermore, adoptions to the MCTS algo- rithm are presented as for example simultaneous decisions, the usage of the Intelligent Driver Model as microscopic traffic simulation, and a cooperative cost function. We further show simulation results of merging scenarios in highway-like situations to underline the cooperative nature of the approach.}, } @inproceedings{, author = {Lenz, David and Kessler, Tobias and Knoll, Alois}, title = {Stochastic Model Predictive Controller with Chance Constraints for Comfortable and Safe Driving Behavior of Autonomous Vehicles}, booktitle = {Proceedings of the IEEE Intelligent Vehicles Symposium}, year = {2015}, month = jun, location = {COEX, Seoul, Korea}, doi = {10.1109/IVS.2015.7225701}, keywords = {autonomous driving, robotics}, } @proceedings{, author = {Minnerup, Pascal and Kessler, Tobias and Knoll, Alois}, title = {Collecting Simulation Scenarios by Analyzing Physical Test Drives}, booktitle = {IEEE International Conference on Intelligent Transportation Systems}, year = {2015}, keywords = {autonomous driving}, }