Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Adaptive Spatio-Temporal Voxels Based Trajectory Planning for Autonomous Driving in Highway Traffic Flow (2310.02625v1)

Published 4 Oct 2023 in cs.RO

Abstract: Trajectory planning is crucial for the safe driving of autonomous vehicles in highway traffic flow. Currently, some advanced trajectory planning methods utilize spatio-temporal voxels to construct feasible regions and then convert trajectory planning into optimization problem solving based on the feasible regions. However, these feasible region construction methods cannot adapt to the changes in dynamic environments, making them difficult to apply in complex traffic flow. In this paper, we propose a trajectory planning method based on adaptive spatio-temporal voxels which improves the construction of feasible regions and trajectory optimization while maintaining the quadratic programming form. The method can adjust feasible regions and trajectory planning according to real-time traffic flow and environmental changes, realizing vehicles to drive safely in complex traffic flow. The proposed method has been tested in both open-loop and closed-loop environments, and the test results show that our method outperforms the current planning methods.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (23)
  1. L. Claussmann, M. Revilloud, D. Gruyer, and S. Glaser, “A review of motion planning for highway autonomous driving,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 5, pp. 1826–1848, 2019.
  2. W. Ding, L. Zhang, J. Chen, and S. Shen, “Safe trajectory generation for complex urban environments using spatio-temporal semantic corridor,” IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2997–3004, 2019.
  3. T. Zhang, W. Song, M. Fu, Y. Yang, X. Tian, and M. Wang, “A unified framework integrating decision making and trajectory planning based on spatio-temporal voxels for highway autonomous driving,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10 365–10 379, 2021.
  4. O. Zheng, M. Abdel-Aty, L. Yue, A. Abdelraouf, Z. Wang, and N. Mahmoud, “Citysim: A drone-based vehicle trajectory dataset for safety oriented research and digital twins,” 2022. [Online]. Available: https://arxiv.org/abs/2208.11036
  5. E. Leurent, “An environment for autonomous driving decision-making,” https://github.com/eleurent/highway-env, 2018.
  6. D. González, J. Pérez, V. Milanés, and F. Nashashibi, “A review of motion planning techniques for automated vehicles,” IEEE Transactions on intelligent transportation systems, vol. 17, no. 4, pp. 1135–1145, 2015.
  7. J. Ziegler, P. Bender, T. Dang, and C. Stiller, “Trajectory planning for bertha—a local, continuous method,” in 2014 IEEE intelligent vehicles symposium proceedings.   IEEE, 2014, pp. 450–457.
  8. M. Werling, J. Ziegler, S. Kammel, and S. Thrun, “Optimal trajectory generation for dynamic street scenarios in a frenet frame,” in 2010 IEEE International Conference on Robotics and Automation.   IEEE, 2010, pp. 987–993.
  9. K. Chu, M. Lee, and M. Sunwoo, “Local path planning for off-road autonomous driving with avoidance of static obstacles,” IEEE transactions on intelligent transportation systems, vol. 13, no. 4, pp. 1599–1616, 2012.
  10. X. Li, Z. Sun, D. Cao, D. Liu, and H. He, “Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles,” Mechanical Systems and Signal Processing, vol. 87, pp. 118–137, 2017.
  11. J. Zhang, Z. Jian, J. Fu, Z. Nan, J. Xin, and N. Zheng, “Trajectory planning with comfort and safety in dynamic traffic scenarios for autonomous driving,” in 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops).   IEEE, 2021, pp. 342–349.
  12. W. Xu, J. Wei, J. M. Dolan, H. Zhao, and H. Zha, “A real-time motion planner with trajectory optimization for autonomous vehicles,” in 2012 IEEE International Conference on Robotics and Automation.   IEEE, 2012, pp. 2061–2067.
  13. J. Villagra, V. Milanés, J. Pérez, and J. Godoy, “Smooth path and speed planning for an automated public transport vehicle,” Robotics and Autonomous Systems, vol. 60, no. 2, pp. 252–265, 2012.
  14. H. Fan, F. Zhu, C. Liu, L. Zhang, L. Zhuang, D. Li, W. Zhu, J. Hu, H. Li, and Q. Kong, “Baidu apollo em motion planner,” arXiv preprint arXiv:1807.08048, 2018.
  15. W. Lim, S. Lee, M. Sunwoo, and K. Jo, “Hybrid trajectory planning for autonomous driving in on-road dynamic scenarios,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 1, pp. 341–355, 2019.
  16. J. Ziegler and C. Stiller, “Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2009, pp. 1879–1884.
  17. W. Ding, L. Zhang, J. Chen, and S. Shen, “Epsilon: An efficient planning system for automated vehicles in highly interactive environments,” IEEE Transactions on Robotics, vol. 38, no. 2, pp. 1118–1138, 2021.
  18. E. M. Gertz and S. J. Wright, “Object-oriented software for quadratic programming,” ACM Transactions on Mathematical Software (TOMS), vol. 29, no. 1, pp. 58–81, 2003.
  19. M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y. Ng, et al., “Ros: an open-source robot operating system,” in ICRA workshop on open source software, vol. 3, no. 3.2.   Kobe, Japan, 2009, p. 5.
  20. M. Sun, F. Baldini, P. Trautman, and T. Murphey, “Move beyond trajectories: Distribution space coupling for crowd navigation,” in Robotics: Science and Systems.   RSS Foundation, 2021, pp. 1–12.
  21. L. Sun, C. Tang, Y. Niu, E. Sachdeva, C. Choi, T. Misu, M. Tomizuka, and W. Zhan, “Domain knowledge driven pseudo labels for interpretable goal-conditioned interactive trajectory prediction,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 13 034–13 041.
  22. L. Sun, W. Zhan, D. Wang, and M. Tomizuka, “Interactive prediction for multiple, heterogeneous traffic participants with multi-agent hybrid dynamic bayesian network,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, pp. 1025–1031.
  23. Z.-H. Yin, L. Sun, L. Sun, M. Tomizuka, and W. Zhan, “Diverse critical interaction generation for planning and planner evaluation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 7036–7043.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now