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Distributed Coordination of Connected and Automated Vehicles at Multiple Interconnected Intersections

Authors: Zhiyuan Du, Baisravan Hom Chaudhuri, Pierluigi Pisu

Abstract:

In connected vehicle systems where wireless communication is available among the involved vehicles and intersection controllers, it is possible to design an intersection coordination strategy that leads the connected and automated vehicles (CAVs) travel through the road intersections without the conventional traffic light control. In this paper, we present a distributed coordination strategy for the CAVs at multiple interconnected intersections that aims at improving system fuel efficiency and system mobility. We present a distributed control solution where in the higher level, the intersection controllers calculate the road desired average velocity and optimally assign reference velocities of each vehicle. In the lower level, every vehicle is considered to use model predictive control (MPC) to track their reference velocity obtained from the higher level controller. The proposed method has been implemented on a simulation-based case with two-interconnected intersection network. Additionally, the effects of mixed vehicle types on the coordination strategy has been explored. Simulation results indicate the improvement on vehicle fuel efficiency and traffic mobility of the proposed method.

Keywords: Connected vehicles, automated vehicles, intersection coordination systems, multiple interconnected intersections, model predictive control.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1124485

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References:


[1] T. S. Facts, "A compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system," National Highway Traffic Safety Administration. DOT HS, 2013.
[2] T. S. Facts, "A compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system," National Highway Traffic Safety Administration. DOT HS, vol. 809, p. 775, 2003.
[3] ITSJPO. (2015). Connected Vehicle Research in the United States.
[4] A. Böhm, "Delay-sensitive wireless communication for cooperative driving applications," 2013.
[5] J. Wright, J. K. Garrett, C. J. Hill, G. D. Krueger, J. H. Evans, S. Andrews, et al., "National Connected Vehicle Field Infrastructure Footprint Analysis," 2014.
[6] K. Dresner and P. Stone, "A multiagent approach to autonomous intersection management," Journal of artificial intelligence research, pp. 591-656, 2008.
[7] Q. Jin, G. Wu, K. Boriboonsomsin, and M. Barth, "Platoon-based multi-agent intersection management for connected vehicle," in Intelligent Transportation Systems-(ITSC), 2013 16th International IEEE Conference on, 2013, pp. 1462-1467.
[8] J. Lee and B. Park, "Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment," Intelligent Transportation Systems, IEEE Transactions on, vol. 13, pp. 81-90, 2012.
[9] Q. Jin, G. Wu, K. Boriboonsomsin, and M. Barth, "Multi-Agent Intersection Management for Connected Vehicles Using an Optimal Scheduling Approach," in Connected Vehicles and Expo (ICCVE), 2012 International Conference on, 2012, pp. 185-190.
[10] M. Kamal, A. Samad, J.-i. Imura, A. Ohata, T. Hayakawa, and K. Aihara, "Coordination of automated vehicles at a traffic-lightless intersection," in Intelligent Transportation Systems-(ITSC), 2013 16th International IEEE Conference on, 2013, pp. 922-927.
[11] L. Makarem and D. Gillet, "Model predictive coordination of autonomous vehicles crossing intersections," in Intelligent Transportation Systems-(ITSC), 2013 16th International IEEE Conference on, 2013, pp. 1799-1804.
[12] J. Rios-Torres, A. Malikopoulos, and P. Pisu, "Online Optimal Control of Connected Vehicles for Efficient Traffic Flow at Merging Roads," Oak Ridge National Laboratory (ORNL); National Transportation Research Center (NTRC)2015.
[13] C. Wuthishuwong and A. Traechtler, "Consensus coordination in the network of Autonomous Intersection Management," in Informatics in Control, Automation and Robotics (ICINCO), 2014 11th International Conference on, 2014, pp. 794-801.
[14] C. Wuthishuwong and A. Traechtler, "Coordination of multiple autonomous intersections by using local neighborhood information," in Connected Vehicles and Expo (ICCVE), 2013 International Conference on, 2013, pp. 48-53.
[15] M. Tlig, O. Buffet, and O. Simonin, "Stop-free strategies for traffic networks: Decentralized on-line optimization," in ECAI 2014-21th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2014), 2014.
[16] M. Hausknecht, T.-C. Au, and P. Stone, "Autonomous intersection management: Multi-intersection optimization," in Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, 2011, pp. 4581-4586.
[17] F. L. Hall, "Traffic stream characteristics," Traffic Flow Theory. US Federal Highway Administration, 1996.
[18] D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O. Scokaert, "Constrained model predictive control: Stability and optimality," Automatica, vol. 36, pp. 789-814, 2000.
[19] L. Grüne and J. Pannek, Nonlinear model predictive control: Springer, 2011.
[20] B. HomChaudhuri, A. Vahidi, and P. Pisu, "A fuel economic model predictive control strategy for a group of connected vehicles in urban roads," pp. 2741-2746, 2015.
[21] B. HomChaudhuri, R. Lin, and P. Pisu, "Hierarchical Control Strategies for Energy Management of Connected Hybrid Electric Vehicles in Urban Roads," to appear in Transportation Research Part C: Emerging Technologies, 2015.
[22] M. Kamal, A. Samad, M. Mukai, J. Murata, and T. Kawabe, "Model predictive control of vehicles on urban roads for improved fuel economy," Control Systems Technology, IEEE Transactions on, vol. 21, pp. 831-841, 2013.