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Safety Metric Aware Trajectory Repairing for Automated Driving

Published 20 Aug 2024 in cs.RO | (2408.10622v1)

Abstract: Recent analyses highlight challenges in autonomous vehicle technologies, particularly failures in decision-making under dynamic or emergency conditions. Traditional automated driving systems recalculate the entire trajectory in a changing environment. Instead, a novel approach retains valid trajectory segments, minimizing the need for complete replanning and reducing changes to the original plan. This work introduces a trajectory repairing framework that calculates a feasible evasive trajectory while computing the Feasible Time-to-React (F-TTR), balancing the maintenance of the original plan with safety assurance. The framework employs a binary search algorithm to iteratively create repaired trajectories, guaranteeing both the safety and feasibility of the trajectory repairing result. In contrast to earlier approaches that separated the calculation of safety metrics from trajectory repairing, which resulted in unsuccessful plans for evasive maneuvers, our work has the anytime capability to provide both a Feasible Time-to-React and an evasive trajectory for further execution.

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References (18)
  1. R. L. McCarthy, “Autonomous vehicle accident data analysis: California ol 316 reports: 2015–2020,” ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, vol. 8, no. 3, 2022.
  2. K. Tong, S. Solmaz, M. Horn, M. Stolz, and D. Watzenig, “Robust tunable trajectory repairing for autonomous vehicles using bernstein basis polynomials and path-speed decoupling,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC).   IEEE, 2023, pp. 8–15.
  3. M. Schratter, M. Hartmann, and D. Watzenig, “Pedestrian collision avoidance system for autonomous vehicles,” SAE International Journal of Connected and Automated Vehicles, vol. 2, no. 4, 2019.
  4. J. Guo, U. Kurup, and M. Shah, “Is it safe to drive? an overview of factors, metrics, and datasets for driveability assessment in autonomous driving,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 8, pp. 3135–3151, 2020.
  5. J. Hillenbrand, A. M. Spieker, and K. Kroschel, “A multilevel collision mitigation approach—its situation assessment, decision making, and performance tradeoffs,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 4, pp. 528–540, 2006.
  6. S. Sontges, M. Koschi, and M. Althoff, “Worst-case analysis of the time-to-react using reachable sets,” in 2018 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2018, pp. 1891–1897.
  7. Y. Lin, S. Maierhofer, and M. Althoff, “Sampling-based trajectory repairing for autonomous vehicles,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC).   IEEE, 2021, pp. 572–579.
  8. Y. Lin and M. Althoff, “Commonroad-crime: A toolbox for criticality measures of autonomous vehicles,” in 2023 IEEE Intelligent Vehicles Symposium (IV), 2023, pp. 1–8.
  9. B. Zhou, F. Gao, L. Wang, C. Liu, and S. Shen, “Robust and efficient quadrotor trajectory generation for fast autonomous flight,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3529–3536, 2019.
  10. X. Zhou, Z. Wang, H. Ye, C. Xu, and F. Gao, “Ego-planner: An esdf-free gradient-based local planner for quadrotors,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 478–485, 2021.
  11. Y. Lin and M. Althoff, “Rule-compliant trajectory repairing using satisfiability modulo theories,” in 2022 IEEE Intelligent Vehicles Symposium (IV), 2022, pp. 449–456.
  12. M. Werling, J. Ziegler, S. Kammel, and S. Thrun, “Optimal trajectory generation for dynamic street scenarios in a frenét frame,” in 2010 IEEE International Conference on Robotics and Automation.   IEEE, 03.05.2010 - 07.05.2010, pp. 987–993.
  13. C. Pek, V. Rusinov, S. Manzinger, M. C. Üste, and M. Althoff, “Commonroad drivability checker: Simplifying the development and validation of motion planning algorithms,” in 2020 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2020, pp. 1013–1020.
  14. M. Althoff, M. Koschi, and S. Manzinger, “Commonroad: Composable benchmarks for motion planning on roads,” in 2017 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2017, pp. 719–726.
  15. K. Tong, S. Solmaz, and M. Horn, “A search-based motion planner utilizing a monitoring functionality for initiating minimal risk maneuvers,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC).   IEEE, 8/10/2022 - 12/10/2022.
  16. S. Deolasee, Q. Lin, J. Li, and J. M. Dolan, “Spatio-temporal motion planning for autonomous vehicles with trapezoidal prism corridors and bézier curves,” in 2023 American Control Conference (ACC).   IEEE, 2023, pp. 3207–3214.
  17. M. Rudigier, S. Solmaz, G. Nestlinger, and K. Tong, “Development, verification and kpi analysis of infrastructure-assisted trajectory planners,” in 2022 International Conference on Connected Vehicle and Expo (ICCVE), 2022, pp. 1–6.
  18. L. Zheng, R. Yang, Z. Peng, H. Liu, M. Y. Wang, and J. Ma, “Real-time parallel trajectory optimization with spatiotemporal safety constraints for autonomous driving in congested traffic,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023, pp. 1186–1193.

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