Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles (2404.12256v1)

Published 18 Apr 2024 in cs.RO, cs.AI, and cs.LG

Abstract: The autonomous driving industry is expected to grow by over 20 times in the coming decade and, thus, motivate researchers to delve into it. The primary focus of their research is to ensure safety, comfort, and efficiency. An autonomous vehicle has several modules responsible for one or more of the aforementioned items. Among these modules, the trajectory planner plays a pivotal role in the safety of the vehicle and the comfort of its passengers. The module is also responsible for respecting kinematic constraints and any applicable road constraints. In this paper, a novel online spatial-temporal graph trajectory planner is introduced to generate safe and comfortable trajectories. First, a spatial-temporal graph is constructed using the autonomous vehicle, its surrounding vehicles, and virtual nodes along the road with respect to the vehicle itself. Next, the graph is forwarded into a sequential network to obtain the desired states. To support the planner, a simple behavioral layer is also presented that determines kinematic constraints for the planner. Furthermore, a novel potential function is also proposed to train the network. Finally, the proposed planner is tested on three different complex driving tasks, and the performance is compared with two frequently used methods. The results show that the proposed planner generates safe and feasible trajectories while achieving similar or longer distances in the forward direction and comparable comfort ride.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  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. S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” The international journal of robotics research, vol. 30, no. 7, pp. 846–894, 2011.
  3. T. Gu, J. Snider, J. M. Dolan, and J.-w. Lee, “Focused trajectory planning for autonomous on-road driving,” in 2013 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2013, pp. 547–552.
  4. M. McNaughton, C. Urmson, J. M. Dolan, and J.-W. Lee, “Motion planning for autonomous driving with a conformal spatiotemporal lattice,” in 2011 IEEE International Conference on Robotics and Automation.   IEEE, 2011, pp. 4889–4895.
  5. Y. Huang, H. Ding, Y. Zhang, H. Wang, D. Cao, N. Xu, and C. Hu, “A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach,” IEEE Transactions on Industrial Electronics, vol. 67, no. 2, pp. 1376–1386, 2019.
  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. A. Broggi, P. Medici, P. Zani, A. Coati, and M. Panciroli, “Autonomous vehicles control in the vislab intercontinental autonomous challenge,” Annual Reviews in Control, vol. 36, no. 1, pp. 161–171, 2012.
  8. P. Petrov and F. Nashashibi, “Modeling and nonlinear adaptive control for autonomous vehicle overtaking,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 4, pp. 1643–1656, 2014.
  9. L. Han, H. Yashiro, H. T. N. Nejad, Q. H. Do, and S. Mita, “Bezier curve based path planning for autonomous vehicle in urban environment,” in 2010 IEEE intelligent vehicles symposium.   IEEE, 2010, pp. 1036–1042.
  10. 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.
  11. M. Geisslinger, F. Poszler, and M. Lienkamp, “An ethical trajectory planning algorithm for autonomous vehicles,” Nature Machine Intelligence, vol. 5, no. 2, pp. 137–144, 2023.
  12. T. Stahl, A. Wischnewski, J. Betz, and M. Lienkamp, “Multilayer graph-based trajectory planning for race vehicles in dynamic scenarios,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC).   IEEE, 2019, pp. 3149–3154.
  13. S. Kammel, J. Ziegler, B. Pitzer, M. Werling, T. Gindele, D. Jagzent, J. Schröder, M. Thuy, M. Goebl, F. v. Hundelshausen et al., “Team annieway’s autonomous system for the 2007 darpa urban challenge,” Journal of Field Robotics, vol. 25, no. 9, pp. 615–639, 2008.
  14. M. Montemerlo, J. Becker, S. Bhat, H. Dahlkamp, D. Dolgov, S. Ettinger, D. Haehnel, T. Hilden, G. Hoffmann, B. Huhnke et al., “Junior: The stanford entry in the urban challenge,” Journal of field Robotics, vol. 25, no. 9, pp. 569–597, 2008.
  15. D. Ferguson, T. M. Howard, and M. Likhachev, “Motion planning in urban environments,” Journal of Field Robotics, vol. 25, no. 11-12, pp. 939–960, 2008.
  16. Z. Han, Y. Wu, T. Li, L. Zhang, L. Pei, L. Xu, C. Li, C. Ma, C. Xu, S. Shen et al., “An efficient spatial-temporal trajectory planner for autonomous vehicles in unstructured environments,” IEEE Transactions on Intelligent Transportation Systems, 2023.
  17. 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.
  18. X. Hu, L. Chen, B. Tang, D. Cao, and H. He, “Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles,” Mechanical systems and signal processing, vol. 100, pp. 482–500, 2018.
  19. Y. Rasekhipour, A. Khajepour, S.-K. Chen, and B. Litkouhi, “A potential field-based model predictive path-planning controller for autonomous road vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 5, pp. 1255–1267, 2016.
  20. N. Noto, H. Okuda, Y. Tazaki, and T. Suzuki, “Steering assisting system for obstacle avoidance based on personalized potential field,” in 2012 15th International IEEE Conference on Intelligent Transportation Systems.   IEEE, 2012, pp. 1702–1707.
  21. X. Chen, Z. Huang, Y. Sun, Y. Zhong, R. Gu, and L. Bai, “Online on-road motion planning based on hybrid potential field model for car-like robot,” Journal of Intelligent & Robotic Systems, vol. 105, no. 1, p. 7, 2022.
  22. I. Sung, B. Choi, and P. Nielsen, “On the training of a neural network for online path planning with offline path planning algorithms,” International Journal of Information Management, vol. 57, p. 102142, 2021.
  23. Z. Huang, H. Liu, J. Wu, and C. Lv, “Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving,” IEEE transactions on neural networks and learning systems, 2023.
  24. L. Yang, C. Lu, G. Xiong, Y. Xing, and J. Gong, “A hybrid motion planning framework for autonomous driving in mixed traffic flow,” Green Energy and Intelligent Transportation, vol. 1, no. 3, p. 100022, 2022.
  25. Á. Fehér, S. Aradi, F. Hegedüs, T. Bécsi, and P. Gáspár, “Hybrid ddpg approach for vehicle motion planning,” 2019.
  26. C.-J. Hoel, K. Driggs-Campbell, K. Wolff, L. Laine, and M. J. Kochenderfer, “Combining planning and deep reinforcement learning in tactical decision making for autonomous driving,” IEEE transactions on intelligent vehicles, vol. 5, no. 2, pp. 294–305, 2019.
  27. H. Krasowski, X. Wang, and M. Althoff, “Safe reinforcement learning for autonomous lane changing using set-based prediction,” in 2020 IEEE 23rd international conference on Intelligent Transportation Systems (ITSC).   IEEE, 2020, pp. 1–7.
  28. PyTorch Geometric, “Heterogeneous graph learning,” [Online]. Available: https://pytorch-geometric.readthedocs.io/en/latest/notes/heter- ogeneous.html, Last Accessed: July 2023.
  29. F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE transactions on neural networks, vol. 20, no. 1, pp. 61–80, 2008.
  30. J. Leskovec, “CS224W: Machine learning with Graphs, Stanford University,” [Online]. Available: http://web.stanford.edu/class/cs224w, Last Accessed: July 2023.
  31. P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” arXiv preprint arXiv:1710.10903, 2017.
  32. PyTorch, “TORCH.FLATTEN,” [Online]. Available: https://pytorch.org/docs/stable/gen- erated/torch.flatten.html, Last Accessed: February 2024.
  33. O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” in Proceedings. 1985 IEEE international conference on robotics and automation, vol. 2.   IEEE, 1985, pp. 500–505.
  34. S. Aradi, “Survey of deep reinforcement learning for motion planning of autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 2, pp. 740–759, 2020.
  35. P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner, “Microscopic traffic simulation using sumo,” in 2018 21st international conference on intelligent transportation systems (ITSC).   IEEE, 2018, pp. 2575–2582.
  36. The MathWorks Inc., “Navigation toolbox version: 2.3 (r2022b),” 2022. [Online]. Available: https://www.mathworks.com/help/stats/index.- html
  37. N. Hogan and D. Sternad, “Sensitivity of smoothness measures to movement duration, amplitude, and arrests,” Journal of motor behavior, vol. 41, no. 6, pp. 529–534, 2009.
  38. X. Li, A. Rakotonirainy, and X. Yan, “How do drivers avoid collisions? a driving simulator-based study,” Journal of safety research, vol. 70, pp. 89–96, 2019.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Jilan Samiuddin (3 papers)
  2. Benoit Boulet (27 papers)
  3. Di Wu (477 papers)
Citations (2)
View on Semantic Scholar

Summary

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

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