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A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety (2403.13251v1)

Published 20 Mar 2024 in cs.RO

Abstract: Lane merging is one of the critical tasks for self-driving cars, and how to perform lane-merge maneuvers effectively and safely has become one of the important standards in measuring the capability of autonomous driving systems. However, due to the ambiguity in driving intentions and right-of-way issues, the lane merging process in autonomous driving remains deficient in terms of maintaining or ceding the right-of-way and attributing liability, which could result in protracted durations for merging and problems such as trajectory oscillation. Hence, we present a rule-compliance path planner (RCPP) for lane-merge scenarios, which initially employs the extended responsibility-sensitive safety (RSS) to elucidate the right-of-way, followed by the potential field-based sigmoid planner for path generation. In the simulation, we have validated the efficacy of the proposed algorithm. The algorithm demonstrated superior performance over previous approaches in aspects such as merging time (Saved 72.3%), path length (reduced 53.4%), and eliminating the trajectory oscillation.

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References (24)
  1. S. Teng, X. Hu, P. Deng, B. Li, Y. Li, Y. Ai, D. Yang, L. Li, Z. Xuanyuan, F. Zhu, and L. Chen, “Motion planning for autonomous driving: The state of the art and future perspectives,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 6, pp. 3692–3711, Jun. 2023.
  2. M. Shawky, “Factors affecting lane change crashes,” IATSS Research, vol. 44, no. 2, pp. 155–161, Jul. 2020.
  3. J. Zhu, S. Easa, and K. Gao, “Merging control strategies of connected and autonomous vehicles at freeway on-ramps: A comprehensive review,” Journal of Intelligent and Connected Vehicles, vol. 5, no. 2, pp. 99–111, 2022.
  4. D. Bevly, X. Cao, M. Gordon, G. Ozbilgin, D. Kari, B. Nelson, J. Woodruff, M. Barth, C. Murray, A. Kurt, K. Redmill, and U. Ozguner, “Lane change and merge maneuvers for connected and automated vehicles: A survey,” IEEE Transactions on Intelligent Vehicles, vol. 1, no. 1, pp. 105–120, Mar. 2016.
  5. X. Hu and J. Sun, “Trajectory optimization of connected and autonomous vehicles at a multilane freeway merging area,” Transportation Research Part C: Emerging Technologies, vol. 101, pp. 111–125, Apr. 2019.
  6. S. Jing, F. Hui, X. Zhao, J. Rios-Torres, and A. J. Khattak, “Cooperative game approach to optimal merging sequence and on-ramp merging control of connected and automated vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 11, pp. 4234–4244, Nov. 2019.
  7. H. Xu, S. Feng, Y. Zhang, and L. Li, “A Grouping-Based cooperative driving strategy for CAVs merging problems,” IEEE Transactions on Vehicular Technology, vol. 68, no. 6, pp. 6125–6136, Jun. 2019.
  8. K. Liu, N. Li, H. Eric Tseng, I. Kolmanovsky, and A. Girard, “Interaction-Aware trajectory prediction and planning for autonomous vehicles in forced merge scenarios,” Dec. 2021.
  9. J. Zhu, I. Tasic, and X. Qu, “Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas,” Multimodal Transportation, vol. 1, no. 1, p. 100005, Mar. 2022.
  10. Y. Ji, L. Ni, C. Zhao, C. Lei, Y. Du, and W. Wang, “TriPField: A 3D potential field model and its applications to local path planning of autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 3, pp. 3541–3554, Mar. 2023.
  11. D. Chen, M. R. Hajidavalloo, Z. Li, K. Chen, Y. Wang, L. Jiang, and Y. Wang, “Deep Multi-Agent reinforcement learning for highway On-Ramp merging in mixed traffic,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 11, pp. 11 623–11 638, Nov. 2023.
  12. S. Arbabi, D. Tavernini, S. Fallah, and R. Bowden, “Decision making for autonomous driving in interactive merge scenarios via learning-based prediction,” Mar. 2023.
  13. R. Ammourah and A. Talebpour, “Deep reinforcement learning approach for automated vehicle mandatory lane changing,” Transportation research record, vol. 2677, no. 2, pp. 712–724, Feb. 2023.
  14. Z. Gu, L. Gao, H. Ma, S. E. Li, S. Zheng, W. Jing, and J. Chen, “Safe-State enhancement method for autonomous driving via direct hierarchical reinforcement learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9966–9983, Sep. 2023.
  15. J. Guo, L. Cheng, and S. Wang, “CoTV: Cooperative control for traffic light signals and connected autonomous vehicles using deep reinforcement learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 10, pp. 10 501–10 512, Oct. 2023.
  16. Y. Gurses, K. Buyukdemirci, and Y. Yildiz, “Developing driving strategies efficiently: A Skill-Based hierarchical reinforcement learning approach,” Feb. 2023.
  17. S. Shalev-Shwartz, S. Shammah, and A. Shashua, “On a formal model of safe and scalable self-driving cars,” Aug. 2017.
  18. B. Gassmann, F. Oboril, C. Buerkle, S. Liu, S. Yan, M. S. Elli, I. Alvarez, N. Aerrabotu, S. Jaber, P. van Beek, D. Iyer, and J. Weast, “Towards standardization of AV safety: C++ library for responsibility sensitive safety,” in 2019 IEEE Intelligent Vehicles Symposium (IV), Jun. 2019, pp. 2265–2271.
  19. S. Liu, X. Wang, O. Hassanin, X. Xu, M. Yang, D. Hurwitz, and X. Wu, “Calibration and evaluation of responsibility-sensitive safety (RSS) in automated vehicle performance during cut-in scenarios,” Transportation Research Part C: Emerging Technologies, vol. 125, p. 103037, Apr. 2021.
  20. M. T. Wolf and J. W. Burdick, “Artificial potential functions for highway driving with collision avoidance,” in 2008 IEEE International Conference on Robotics and Automation, May 2008, pp. 3731–3736.
  21. J. Ji, A. Khajepour, W. W. Melek, and Y. Huang, “Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints,” IEEE Transactions on Vehicular Technology, vol. 66, no. 2, pp. 952–964, Feb. 2017.
  22. P. Lin, Y. S. Quan, J. H. Yang, C. C. Chung, and M. Tsukada, “Safety Tunnel-Based model predictive Path-Planning controller with potential functions for emergency navigation,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 4, pp. 3974–3985, Apr. 2023.
  23. G. Cesari, G. Schildbach, A. Carvalho, and F. Borrelli, “Scenario model predictive control for lane change assistance and autonomous driving on highways,” IEEE Intelligent Transportation Systems Magazine, vol. 9, no. 3, pp. 23–35, 2017, doi:10.1109/MITS.2017.2709782.
  24. B. Lu, H. He, H. Yu, H. Wang, G. Li, M. Shi, and D. Cao, “Hybrid path planning combining potential field with sigmoid curve for autonomous driving,” Sensors, vol. 20, no. 24, Dec. 2020.

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