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Graph-Based Modeling, Scheduling, and Verification for Intersection Management of Intelligent Vehicles

Published:08 October 2019Publication History
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Abstract

Intersection management is one of the most representative applications of intelligent vehicles with connected and autonomous functions. The connectivity provides environmental information that a single vehicle cannot sense, and the autonomy supports precise vehicular control that a human driver cannot achieve. Intersection management solves the fundamental conflict resolution problem for vehicles—two vehicles should not appear at the same location at the same time, and, if they intend to do that, an order should be decided to optimize certain objectives such as the traffic throughput or smoothness. In this paper, we first propose a graph-based model for intersection management. The model is general and applicable to different granularities of intersections and other conflicting scenarios. We then derive formal verification approaches which can guarantee deadlock-freeness. Based on the graph-based model and the verification approaches, we develop a centralized cycle removal algorithm for the graph-based model to schedule vehicles to go through the intersection safely (without collisions) and efficiently without deadlocks. Experimental results demonstrate the expressiveness of the proposed model and the effectiveness and efficiency of the proposed algorithm.

References

  1. M. Ahmane, A. Abbas-Turki, F. Perronnet, J. Wu, A. El Moudni, J. Buisson, and R. Zeo. 2013. Modeling and controlling an isolated urban intersection based on cooperative vehicles. Transportation Research Part C: Emerging Technologies 28 (2013), 44--62.Google ScholarGoogle ScholarCross RefCross Ref
  2. H. Ahn and D. Del Vecchio. 2016. Semi-autonomous intersection collision avoidance through job-shop scheduling. In ACM International Conference on Hybrid Systems: Computation and Control (HSCC). 185--194.Google ScholarGoogle Scholar
  3. S. R. Azimi, G. Bhatia, R. R. Rajkumar, and P. Mudalige. 2013. Reliable intersection protocols using vehicular networks. In ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS). 1--10.Google ScholarGoogle Scholar
  4. S. R. Azimi, G. Bhatia, R. R. Rajkumar, and P. Mudalige. 2014. STIP: Spatio-temporal intersection protocols for autonomous vehicles. In ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS). 1--12.Google ScholarGoogle Scholar
  5. L. Chen and C. Englund. 2016. Cooperative intersection management: A survey. IEEE Transactions on Intelligent Transportation Systems 17, 2 (2016), 570--586.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. E. G. Coffman, M. Elphick, and A. Shoshani. 1971. System deadlocks. Comput. Surveys 3, 2 (1971), 67--78.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. Colombo and D. Del Vecchio. 2015. Least restrictive supervisors for intersection collision avoidance: A scheduling approach. IEEE Trans. Automat. Control 60, 6 (2015), 1515--1527.Google ScholarGoogle ScholarCross RefCross Ref
  8. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. 2009. Introduction to Algorithms, Third Edition (3rd ed.). The MIT Press.Google ScholarGoogle Scholar
  9. E. Dallal, A. Colombo, D. Del Vecchio, and S. Lafortune. 2013. Supervisory control for collision avoidance in vehicular networks using discrete event abstractions. In American Control Conference. 4380--4386.Google ScholarGoogle Scholar
  10. N. J. Dingle, W. J. Knottenbelt, and T. Suto. 2009. PIPE2: A tool for the performance evaluation of generalised stochastic Petri nets. ACM Performance Evaluation Review 36, 4 (2009), 34--39.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. K. Dresner and P. Stone. 2008. A multiagent approach to autonomous intersection management. Journal of Artificial Intelligence Research 31 (2008), 591--656.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Q. Jin, G. Wu, K. Boriboonsomsin, and M. Barth. 2012. Advanced intersection management for connected vehicles using a multi-agent systems approach. In IEEE Intelligent Vehicles Symposium. 932--937.Google ScholarGoogle Scholar
  13. V. Kann. 1992. On the approximability of NP-complete optimization problems. Ph.D. Thesis, Department of Numerical Analysis and Computing Science, Royal Institute of Technology, Stockholm (1992).Google ScholarGoogle Scholar
  14. R. M. Karp. 1972. Reducibility among combinatorial problems. In Complexity of Computer Computations. The IBM Research Symposia Series. 85--103.Google ScholarGoogle Scholar
  15. J. Kleinberg and E. Tardos. 2006. Algorithm Design. Pearson, Addison Wesley.Google ScholarGoogle Scholar
  16. H. Kowshik, D. Caveney, and P. R. Kumar. 2011. Provable systemwide safety in intelligent intersections. IEEE Transactions on Vehicular Technology 60, 3 (2011), 804--818.Google ScholarGoogle ScholarCross RefCross Ref
  17. J. B. Kruskal. 1956. On the shortest spanning subtree of a graph and the traveling salesman problem. American Mathematical Society 7, 1 (1956), 48--50.Google ScholarGoogle ScholarCross RefCross Ref
  18. C. Liu, C. Lin, S. Shiraishi, and M. Tomizuka. 2018. Distributed conflict resolution for connected autonomous vehicles. IEEE Transactions on Intelligent Vehicles 3, 1 (2018), 18--29.Google ScholarGoogle ScholarCross RefCross Ref
  19. D. A. Menasce and R. R. Muntz. 1979. Locking and deadlock detection in distributed data bases. IEEE Transactions on Software Engineering SE-5, 3 (1979), 195--202.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. R. Naumann, R. Rasche, J. Tacken, and C. Tahedi. 1997. Validation and simulation of a decentralized intersection collision avoidance algorithm. In International Conference on Intelligent Transportation Systems. 818--823.Google ScholarGoogle Scholar
  21. J. L. Peterson. 1977. Petri nets. Comput. Surveys 9, 3 (1977).Google ScholarGoogle Scholar
  22. S. Reveliotis and E. Roszkowska. 2011. Conflict resolution in free-ranging multivehicle systems: a resource allocation paradigm. IEEE Transactions on Robotics 27, 2 (2011), 283--296.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. M. Singhal. 1989. Deadlock detection in distributed systems. Computer 22, 11 (1989), 37--48.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. J. Wu, F. Perronnet, and A. Abbas-Turki. 2014. Cooperative vehicle-actuator system: A sequence-based framework of cooperative intersections management. IET Intelligent Transport Systems 8, 4 (2014), 352--360.Google ScholarGoogle ScholarCross RefCross Ref
  25. D. H. Younger. 1963. Minimum feedback arc sets for a directed graph. IEEE Transactions on Circuit Theory 10, 2 (1963), 238--245.Google ScholarGoogle ScholarCross RefCross Ref
  26. B. Zheng, C. Lin, H. Liang, S. Shiraishi, W. Li, and Q. Zhu. 2017. Delay-aware design, analysis and verification of intelligent intersection management. In IEEE International Conference on Smart Computing (SMARTCOMP). 1--8.Google ScholarGoogle Scholar
  27. F. Zhu and S. V. Ukkusuri. 2015. A linear programming formulation for autonomous intersection control within a dynamic traffic assignment and connected vehicle environment. Transportation Research Part C: Emerging Technologies 55 (2015), 363--378.Google ScholarGoogle ScholarCross RefCross Ref

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