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Optimization-based motion primitive automata for autonomous driving (2401.14276v1)

Published 25 Jan 2024 in eess.SY, cs.RO, and cs.SY

Abstract: 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.

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References (12)
  1. K. Flaßkamp, On the Optimal Control of Mechanical Systems – Hybrid Control Strategies and Hybrid Dynamics. PhD thesis, University of Paderborn, 2013.
  2. M. Kloock, P. Scheffe, J. Maczijewski, A. Kampmann, A. Mokhtarian, S. Kowalewski, and B. Alrifaee, “Cyber-Physical Mobility Lab: An open-source platform for networked and autonomous vehicles,” in 2021 European Control Conference (ECC), pp. 1937–1944, 2021.
  3. E. Frazzoli, M. A. Dahleh, and E. Feron, “Maneuver-based motion planning for nonlinear systems with symmetries,” IEEE Transactions on Robotics, vol. 21, no. 6, pp. 1077–1091, 2005.
  4. M. Althoff, M. Koschi, and S. Manzinger, “Commonroad: Composable benchmarks for motion planning on roads,” in Proc. of the IEEE Intelligent Vehicles Symposium, 2017.
  5. P. Scheffe, M. V. A. Pedrosa, K. Flaßkamp, and B. Alrifaee, “Receding horizon control using graph search for multi-agent trajectory planning,” IEEE Transactions on Control Systems Technology, pp. 1–14, 2022.
  6. M. V. A. Pedrosa, T. Schneider, and K. Flaßkamp, “Learning motion primitives automata for autonomous driving applications,” Mathematical and Computational Applications, vol. 27, no. 4, 2022.
  7. K. Flaßkamp, S. Ober-Blöbaum, and K. Worthmann, “Symmetry and motion primitives in model predictive control,” Math. Control. Signals Syst., vol. 31, pp. 455–485, 2019.
  8. M. V. A. Pedrosa, T. Schneider, and K. Flaßkamp, “Graph-based motion planning with primitives in a continuous state space search,” in 2021 6th International Conference on Mechanical Engineering and Robotics Research (ICMERR), pp. 30–39, 2021.
  9. M. Naumann, M. Lauer, and C. Stiller, “Generating comfortable, safe and comprehensible trajectories for automated vehicles in mixed traffic,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 575–582, 2018.
  10. P. Scheffe, G. Dorndorf, and B. Alrifaee, “Increasing feasibility with dynamic priority assignment in distributed trajectory planning for road vehicles,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pp. 3873–3879, 2022.
  11. B. Alrifaee, F.-J. Heßeler, and D. Abel, “Coordinated non-cooperative distributed model predictive control for decoupled systems using graphs,” IFAC-PapersOnLine, vol. 49, no. 22, pp. 216–221, 2016.
  12. P. Scheffe, J. Maczijewski, M. Kloock, A. Kampmann, A. Derks, S. Kowalewski, and B. Alrifaee, “Networked and autonomous model-scale vehicles for experiments in research and education,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 17332–17337, 2020.

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