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Feasibility Study of Distributed Lightless Intersection Control with Level 1 Autonomous Vehicles

Authors: Bo Yang, Christopher Monterola


Urban intersection control without the use of the traffic light has the potential to vastly improve the efficiency of the urban traffic flow. For most proposals in the literature, such lightless intersection control depends on the mass market commercialization of highly intelligent autonomous vehicles (AV), which limits the prospects of near future implementation. We present an efficient lightless intersection traffic control scheme that only requires Level 1 AV as defined by NHTSA. The technological barriers of such lightless intersection control are thus very low. Our algorithm can also accommodate a mixture of AVs and conventional vehicles. We also carry out large scale numerical analysis to illustrate the feasibility, safety and robustness, comfort level, and control efficiency of our intersection control scheme.

Keywords: Intersection control, autonomous vehicles, traffic modelling, intelligent transport system.

Digital Object Identifier (DOI):

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[1] Y. Xuan, C.F. Daganzo, M.J. Cassidy, Increasing the capacity of signalized intersections with separate left turn phases, Transportation Research Part B 45 (2011) 769-781.
[2] D. Helbing and A. Mazloumian, Operation regimes and slower-is-faster effect in the controlof traffic intersections, Eur. Phys. J. B. 70, 257 (2009).
[3] Y. Jiang, S. Li and D.E. Shamo, A platoon-based traffic signal timing algorithm for major? minor intersection types, Transportation Research Part B 40 (2006) 543-562.
[4] S. Lammer and D. Helbing, Self-Stabilizing Decentralized Signal Control of Realistic, Saturated Network Traffic, Sante Fe Institute Working Paper, 2010-09-019.
[5] D.A. Roozemond, Using intelligent agents for pro-active, real-time urban intersection control, Eur. J. Oper. Res. 131, 293 (2001).
[6] Anna L. C. Bazzan, A distributed approach for coordination of traffic signal agents, Autonomous Agents and Multi-Agent Systems, 10, 131 (2005).
[7] M.C. Choy, D. Srinivasan and R. L. Cheu, Cooperative, hybrid agent architecture for real-time traffic signal control, IEEE T. Syst. Man. Cy. A. 33, 597 (2003).
[8] D. Srinivasan, M.C. Choy and R.L. Cheu, Neural networks for real-time traffic signal control, IEEE T. Intell. Trans. Sys. 7, 261 (2006).
[9] Ana L.C. Bazzan, D. de Oliveira and B.C. da Silva, Learning in groups of traffic signals, Eng. Appl. Artif. Intel. 23, 560 (2010).
[10] T-H. Chang and G-Y Sun, Optimal signal timing for an oversaturated intersection, Transportation Research Part B 38 (2004) 687-707.
[11] Perronnet, F.; Abbas-Turki, A.; Buisson, J.; El Moudni, A.; Renan Zeo; Ahmane, M., Cooperative intersection management: Real implementation and feasibility study of a sequence based protocol for urban applications, Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on vol., no., pp.42,47, 16-19 Sept. 2012
[12] J. Wu, A. Abbas-Turki and A. El-Moudni, Cooperative driving: an ant colony system for autonomous intersection management, Applied Intelligence, Springer, pp. 1-16, October 2011.
[13] M. Ahmane, A. Abbas-Turki, F. Perronnet, J. Wu, A. El Moudni, J. Buisson and R. Zeo, Modeling and controlling an isolated urban intersection based on cooperative vehicles, Trans. Res. C., 28, 44(2013).
[14] G. Raravi, V. Shingde, K. Ramamritham and J. Bharadia, Merge algorithms for intelligent vehicles, Next Generation Design and Verification Methodologies for Distributed Embedded Control Systems, 51 (2007).
[15] K. Dresner and P. Stone, A multiagent approach to autonomous intersection management, J. Artif. Intell. Res. 31, 591 (2008).
[16] L. Panait and S. Luke, Cooperative multi-agent learning: The state of the art, Auton. Agent. Multi. Agent. Syst. 11, 387 (2005).
[17] R. Junges and Ana L.C. Bazzan, Evaluating the performance of DCOP algorithms in a real world, dynamic problem, Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, 2, 599 (2008).
[18] U.S. Department of Transportation Releases Policy on Automated Vehicle Development, National Highway Traffic Safety Administration. 30 May 2013
[19] M. Gaciarz, S. Aknine and N. Bhouri, A Continuous Negotiation Based Model for Traffic Regulation at an Intersection, Automated Negotiation for Traffic Regulation, May 2015, Istanbul, Turkey.
[20] M. Crunewald, C. Rust and U. Withowski, Using mini robots for prototyping intersection management of vehicles, Proceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE 2005), 287 (2006).
[21] Naumann, R.; Rasche, R.; Tacken, J.; Tahedi, C., Validation and simulation of a decentralized intersection collision avoidance algorithm, Intelligent Transportation System, 1997. ITSC '97., IEEE Conference on vol., no., pp.818,823, 9-12 Nov 1997.
[22] M. Treiber, A. Hennecke and D. Helbing, Congested traffic states in empirical observations and microscopic simulations, Phys. Rev. E. 62, 1805 (2000).
[23] M. Treiber and A. Kesting, Traffic Flow Dynamics, Springer-Verlag Berlin Heidelberg 2013, and the references therein.
[24] K. Yi and J. Chung, “Nonlinear Brake Control for Vehicle CW/CA Systems”, IEEE/ASME Transactions on Mechatronics, vol. 6, no. 1, pp 17 – 25, March 2001.
[25] B.S. Kerner, Introduction to Modern Traffic Flow Theory and Control: The Long Road to Three-phase Traffic Theory, Springer-Verlag Berlin Heidelberg 2009, and the references therein.