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A Universal Cooperative Decision-Making Framework for Connected Autonomous Vehicles with Generic Road Topologies (2401.04968v1)

Published 10 Jan 2024 in cs.RO, cs.SY, and eess.SY

Abstract: Cooperative decision-making of Connected Autonomous Vehicles (CAVs) presents a longstanding challenge due to its inherent nonlinearity, non-convexity, and discrete characteristics, compounded by the diverse road topologies encountered in real-world traffic scenarios. The majority of current methodologies are only applicable to a single and specific scenario, predicated on scenario-specific assumptions. Consequently, their application in real-world environments is restricted by the innumerable nature of traffic scenarios. In this study, we propose a unified optimization approach that exhibits the potential to address cooperative decision-making problems related to traffic scenarios with generic road topologies. This development is grounded in the premise that the topologies of various traffic scenarios can be universally represented as Directed Acyclic Graphs (DAGs). Particularly, the reference paths and time profiles for all involved CAVs are determined in a fully cooperative manner, taking into account factors such as velocities, accelerations, conflict resolutions, and overall traffic efficiency. The cooperative decision-making of CAVs is approximated as a mixed-integer linear programming (MILP) problem building on the DAGs of road topologies. This favorably facilitates the use of standard numerical solvers and the global optimality can be attained through the optimization. Case studies corresponding to different multi-lane traffic scenarios featuring diverse topologies are scheduled as the test itineraries, and the efficacy of our proposed methodology is corroborated.

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References (35)
  1. X. Sun, F. R. Yu, and P. Zhang, “A survey on cyber-security of connected and autonomous vehicles (CAVs),” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 6240–6259, 2022.
  2. A. Chehri, N. Quadar, and R. Saadane, “Communication and localization techniques in vanet network for intelligent traffic system in smart cities: a review,” Smart Transportation Systems 2020, pp. 167–177, 2020.
  3. Z. Huang, S. Shen, and J. Ma, “Decentralized iLQR for cooperative trajectory planning of connected autonomous vehicles via dual consensus admm,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 11, pp. 12 754–12 766, 2023.
  4. X. Zhang, Z. Cheng, J. Ma, S. Huang, F. L. Lewis, and T. H. Lee, “Semi-definite relaxation-based ADMM for cooperative planning and control of connected autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 9240–9251, 2022.
  5. J. Rios-Torres and A. A. Malikopoulos, “Automated and cooperative vehicle merging at highway on-ramps,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 4, pp. 780–789, 2016.
  6. H. Pei, S. Feng, Y. Zhang, and D. Yao, “A cooperative driving strategy for merging at on-ramps based on dynamic programming,” IEEE Transactions on Vehicular Technology, vol. 68, no. 12, pp. 11 646–11 656, 2019.
  7. P. Hang, C. Huang, Z. Hu, Y. Xing, and C. Lv, “Decision making of connected automated vehicles at an unsignalized roundabout considering personalized driving behaviours,” IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 4051–4064, 2021.
  8. E. G. Debada and D. Gillet, “Virtual vehicle-based cooperative maneuver planning for connected automated vehicles at single-lane roundabouts,” IEEE Intelligent Transportation Systems Magazine, vol. 10, no. 4, pp. 35–46, 2018.
  9. Y. Meng, L. Li, F.-Y. Wang, K. Li, and Z. Li, “Analysis of cooperative driving strategies for nonsignalized intersections,” IEEE Transactions on Vehicular Technology, vol. 67, no. 4, pp. 2900–2911, 2017.
  10. L. Wei, Z. Li, J. Gong, C. Gong, and J. Li, “Autonomous driving strategies at intersections: Scenarios, state-of-the-art, and future outlooks,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC).   IEEE, 2021, pp. 44–51.
  11. B. Toghi, R. Valiente, D. Sadigh, R. Pedarsani, and Y. P. Fallah, “Cooperative autonomous vehicles that sympathize with human drivers,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 4517–4524.
  12. G.-P. Antonio and C. Maria-Dolores, “Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrow’s intersections,” IEEE Transactions on Vehicular Technology, vol. 71, no. 7, pp. 7033–7043, 2022.
  13. M. Zhou, Y. Yu, and X. Qu, “Development of an efficient driving strategy for connected and automated vehicles at signalized intersections: A reinforcement learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 1, pp. 433–443, 2019.
  14. B. Peng, M. F. Keskin, B. Kulcsár, and H. Wymeersch, “Connected autonomous vehicles for improving mixed traffic efficiency in unsignalized intersections with deep reinforcement learning,” Communications in Transportation Research, vol. 1, p. 100017, 2021.
  15. J. Ding, L. Li, H. Peng, and Y. Zhang, “A rule-based cooperative merging strategy for connected and automated vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 8, pp. 3436–3446, 2019.
  16. X. Pan, B. Chen, S. Timotheou, and S. A. Evangelou, “A convex optimal control framework for autonomous vehicle intersection crossing,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 163–177, 2022.
  17. Y. Zhang, R. Hao, T. Zhang, X. Chang, Z. Xie, and Q. Zhang, “A trajectory optimization-based intersection coordination framework for cooperative autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 14 674–14 688, 2021.
  18. H. Xu, Y. Zhang, L. Li, and W. Li, “Cooperative driving at unsignalized intersections using tree search,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 11, pp. 4563–4571, 2019.
  19. L. Xu, J. Lu, B. Ran, F. Yang, and J. Zhang, “Cooperative merging strategy for connected vehicles at highway on-ramps,” Journal of Transportation Engineering, Part A: Systems, vol. 145, no. 6, p. 04019022, 2019.
  20. C. Liu, C.-W. Lin, S. Shiraishi, and M. Tomizuka, “Distributed conflict resolution for connected autonomous vehicles,” IEEE Transactions on Intelligent Vehicles, vol. 3, no. 1, pp. 18–29, 2017.
  21. V. G. Lopez, F. L. Lewis, M. Liu, Y. Wan, S. Nageshrao, and D. Filev, “Game-theoretic lane-changing decision making and payoff learning for autonomous vehicles,” IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 3609–3620, 2022.
  22. Q. Zhang, R. Langari, H. E. Tseng, D. Filev, S. Szwabowski, and S. Coskun, “A game theoretic model predictive controller with aggressiveness estimation for mandatory lane change,” IEEE Transactions on Intelligent Vehicles, vol. 5, no. 1, pp. 75–89, 2019.
  23. P. Hang, C. Huang, Z. Hu, and C. Lv, “Decision making for connected automated vehicles at urban intersections considering social and individual benefits,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 22 549–22 562, 2022.
  24. P. Hang, C. Lv, C. Huang, Y. Xing, and Z. Hu, “Cooperative decision making of connected automated vehicles at multi-lane merging zone: A coalitional game approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 3829–3841, 2021.
  25. D. Monderer and L. S. Shapley, “Potential games,” Games and economic behavior, vol. 14, no. 1, pp. 124–143, 1996.
  26. M. Liu, I. Kolmanovsky, H. E. Tseng, S. Huang, D. Filev, and A. Girard, “Potential game based decision-making frameworks for autonomous driving,” arXiv preprint arXiv:2201.06157, 2022.
  27. C. Burger and M. Lauer, “Cooperative multiple vehicle trajectory planning using MIQP,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC).   IEEE, 2018, pp. 602–607.
  28. K. Esterle, T. Kessler, and A. Knoll, “Optimal behavior planning for autonomous driving: A generic mixed-integer formulation,” in 2020 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2020, pp. 1914–1921.
  29. F. Fabiani and S. Grammatico, “Multi-vehicle automated driving as a generalized mixed-integer potential game,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1064–1073, 2019.
  30. S. A. Fayazi and A. Vahidi, “Mixed-integer linear programming for optimal scheduling of autonomous vehicle intersection crossing,” IEEE Transactions on Intelligent Vehicles, vol. 3, no. 3, pp. 287–299, 2018.
  31. R. A. Dollar and A. Vahidi, “Multilane automated driving with optimal control and mixed-integer programming,” IEEE Transactions on Control Systems Technology, vol. 29, no. 6, pp. 2561–2574, 2021.
  32. T. Kessler and A. Knoll, “Cooperative multi-vehicle behavior coordination for autonomous driving,” in 2019 IEEE Intelligent Vehicles Symposium (IV).   IEEE, 2019, pp. 1953–1960.
  33. Y. Tassa, N. Mansard, and E. Todorov, “Control-limited differential dynamic programming,” in 2014 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2014, pp. 1168–1175.
  34. Gurobi Optimization, LLC, “Gurobi Optimizer Reference Manual,” 2023. [Online]. Available: https://www.gurobi.com
  35. J. A. Andersson, J. Gillis, G. Horn, J. B. Rawlings, and M. Diehl, “CasADi: a software framework for nonlinear optimization and optimal control,” Mathematical Programming Computation, vol. 11, pp. 1–36, 2019.
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