Smart intersections for controlling driverless vehicles. Local coordination and global optimization

Smart intersections for controlling driverless vehicles. Local coordination and global optimization

Mohamed Thig Olivier Buffet Olivier Simonin 

Université de Lorraine, INRIA, CNRS Nancy, France

Université de Lyon, INSA Lyon, INRIA, CITI,69621 Villeurbanne, France

Corresponding Author Email:,
30 June 2016
| Citation



T. In this paper, we address the problem of traffic management in a road network for driverless vehicles, based on local decisions in each intersection. Intersections perceive and control approaching vehicles, through infrastructure-to-vehicle communications, in order to ensure a coordinated and stop-free crossing. Our approach is original in two ways: on the one hand, it explores a principle alternating flows at intersections, and, on the other hand, it proposes distributed algorithms that optimize the global traffic in the network. We present the modeling choices, the algorithms, and the simulation study of our approach, and we compare its performances with existing approaches.


multi-agent systems, autonomous vehicles, traffic simulations

1. Introduction
2. Approches existantes pour la gestion d'intersections
3. Passage alterné sans arrét dans les intersections : approche Alt
4. Coordination et optimisation distribuée d'un réseau d'intersections
S. Résultats expérimentaux
6. Conclusion

Balan G., Luke S. (2006). History-based traffic control. In Proceedings of the fifth International Conference on Autonomous Agents and Multiagent Systems, p. 616–621.

Bazzan A. (2005). A distributed approach for coordination of traffic signal agents. Autonomous Agents and Multi-Agent Systems, vol. 10, no 1, p. 131–164.

Bazzan A. (2009). Opportunities for multiagent systems and multiagent reinforcement learning in traffic control. Autonomous Agents and Multi-Agent Systems, vol. 18, no 3, p. 342-375.

Bhouri N., Balbo F., Pinson S., Tlig M. (2011, aug.). Collaborative agents for modeling traffic regulation systems. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2011

IEEE/WIC/ACM International Conference on, vol. 2, p. 7–13.

Bonhomme A., Mathieu P., Picault S. (2014). A versatile description framework for modeling behaviors in traffic simulations. In International Conference on Tools with Artificial

Intelligence, (ICTAI’14), p. 937–944.

Brockfeld E., Barlovic R., Schadschneider A., Schreckenberg M. (2001). Optimizing traffic lights in a cellular automaton model for city traffic. Physical Review E, vol. 64, no 5.

Bull L., Sha’Aban J., Tomlinson A., Addison J., Heydecker B. (2004). Towards distributed adaptive control for road traffic junction signals using learning classifier systems. In Applications of Learning Classifier Systems, p. 276–299. Springer Berlin Heidelberg.

Camponogara E., Kraus Jr W. (2003). Distributed learning agents in urban traffic control. In Progress in Artificial Intelligence, p. 324–335. Springer.

Carlino D., Boyles S., Stone P. (2013). Auction-based autonomous intersection management. In Intelligent Transportation Systems - (ITSC), 16th International IEEE Conference on.

Da Silva B., Basso E., Bazzan A., Engel P. (2006). Dealing with non-stationary environments using context detection. In Proceedings of the 23rd international conference on Machine

learning, p. 217–224.

De Oliveira D., Bazzan A. (2006). Traffic lights control with adaptive group formation based on swarm intelligence. In Ant Colony Optimization and Swarm Intelligence, p. 520–521.

De Oliveira D., Bazzan A. (2007). Swarm intelligence applied to traffic lights group formation. Anais do VI Encontro Nacional de Inteligência Artificial (ENIA), p. 1003–1112.

Doniec A., Espié S., Mandiau R., Piechowiak S. (2005). Dealing with multi-agent coordination by anticipation : Application to the traffic simulation at junctions. In Proceedings of the

Third European Workshop on Multi-Agent Systems (EUMAS).

Dresner K., Stone P. (2004). Multiagent traffic management : A reservation-based intersection control mechanism. In Proceedings of the Third International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Dresner K., Stone P. (2005). Multiagent traffic management : An improved intersection control mechanism. In Proceedings of the Fourth International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Dresner K., Stone P. (2008). A multiagent approach to autonomous intersection management. Journal of artificial intelligence research, p. 591–656.

Espié S. (1995). Archisim, multi-actor parallel architecture for traffic simulation. In Proceedings of the Second World Congress on Intelligent Transport Systems, vol. 4.

Fabiunke M. (1999). Parallel distributed constraint satisfaction. In Proc. of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA).

Ferreira E., Khosla P. (2000). Multi agent collaboration using distributed value functions. In Intelligent Vehicles Symposium, 2000. IV 2000. Proceedings of the IEEE, p. 404–409.

Fitzpatrick S., Meertens L. (2001). An experimental assessment of a stochastic, anytime, decentralized, soft colourer for sparse graphs. In Proc. 1st Symp. on Stochastic Algorithms :

Foundations and Applications.

Fok C., Hanna M., Gee S., Au T., Stone P., Julien C. et al. (2012). A platform for evaluating autonomous intersection management policies. In Third International Conference on

Cyber-Physical Systems (ICCPS), p. 87–96.

France J., Ghorbani A. (2003). A multiagent system for optimizing urban traffic. In Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on, p. 411–414.

Gechter F., Contet J., Lamotte O., Galland S., Koukam A. (2012, mai). Virtual intelligent vehicle urban simulator : Application to vehicle platoon evaluation. Simulation Modelling

Practice and Theory (SIMPAT), vol. 24, p. 103-114.

Gershenson C. (2004). Self-organizing traffic lights. complex Systems, vol. 16, no 1, p. 29–53.

Guler S. I., Menendez M., Meier L. (2014). Using connected vehicle technology to improve the efficiency of intersections. Transportation Research Part C : Emerging Technologies,

vol. 46, p. 121–131.

Henry J., Farges J., Tuffal J. (1984). The PRODYN real time traffic algorithm. In 4th IFAC/IFIP/IFORS Conference On Control.

Hounsell N., Shrestha B. (2012). A new approach for co-operative bus priority at traffic signals. Intelligent Transportation Systems, vol. 13, no 1, p. 6 -14.

Hunt P. B., Robertson D. I., Bretherton R. D., Winton R. I. (1981). SCOOT - A Traffic Responsive Method of Coordinating Signals. Rapport technique. TRRL.

Köhler E., Möhring R., Wünsch G. (2005). Minimizing total delay in fixed-time controlled traffic networks. In Operations Research Proceedings 2004, p. 192–199.

Kosonen I. (2003). Multi-agent fuzzy signal control based on real-time simulation. Transportation Research Part C : Emerging Technologies, vol. 11, no 5, p. 389–403.

Lowrie P. (1982). The sydney coordinated adaptive traffic system-principles, methodology, algorithms. In International Conference on Road Traffic Signalling, 1982, London, UK.

Mohring R., Nokel K., Wunsch G. (2006). A model and fast optimization method for signal coordination in a network. In Control in Transportation Systems, p. 73–78.

Naumann R., Rasche R. (1997). Intersection collision avoidance by means of decentralized security and communication management of autonomous vehicles. Univ.-GH, SFB 376.

Naumann R., Rasche R., Tacken J., Tahedi C. (1997). Validation and simulation of a decentralized intersection collision avoidance algorithm. In Intelligent Transportation System

(ITSC’97), IEEE, p. 818–823.

Nunes L., Oliveira E. (2004). Learning from multiple sources. In Proceedings of the Third International Conference on Autonomous Agents and Multiagent Systems, p. 1106–1113.

Richter S., Aberdeen D., Yu J. (2007). Natural actor-critic for road traffic optimization. In B. Schölkopf, J. Platt, T. Hofmann (Eds.), Advances in Neural Information Processing Systems

19. Cambridge, MA, MIT Press.

Robertson D. (1969). TRANSYT : A Traffic Network Study Tool. Road Research Laboratory.

Rochner F., Prothmann H., Branke J., Müller-Schloer C., Schmeck H. (2006). An organic architecture for traffic light controllers. In Proceedings of the Informatik 2006 –Informatik

fürMenschen–I Jahrestagung (1), p. 120–127.

Russell S., Norvig P. (1995). Artificial Intelligence : A Modern Approach. Englewood Cliffs, NJ : prentice Hall.

Sánchez J., Aguirre J. (2007). Traffic light control through agent-based coordination. In Artificial Intelligence and Applications, p. 163–168.

Scheuer A., Simonin O., Charpillet F. (2009). Safe longitudinal platoons of vehicles without communication. In Proceedings of the International Conference on Robotics and Automation (ICRA), p. 2835–2840.

Steingrover M., Schouten R., Peelen S., Nijhuis E., Bakker B. (2005). Reinforcement learning of traffic light controllers adapting to traffic congestion. In Proceedings of the Belgium-

Netherlands Artificial Intelligence Conference (BNAIC), p. 216–223.

Tlig M. (2015). Coordination locale et optimisation distribuée du trafic de véhicules autonomes dans un réseau routier. Thèse, Université de Lorraine. (https ://

Treiber M., Kesting A. (2010). An open-source microscopic traffic simulator. Intelligent Transportation Systems Magazine, IEEE, vol. 2, no 3, p. 6–13.

Weixiong Z., GuandongW., Zhao X., LarsW. (2005). Distributed stochastic search and distributed breakout : properties, comparison and applications to constraint optimization problems in sensor networks. Artificial Intelligence, vol. 161, no 1–2, p. 55–87.

Wiering M. (2000). Multi-agent reinforcement learning for traffic light control. In Proceedings of the International Conference on Machine Learning, p. 1151–1158.

Yin B., Dridi M., El Moudni A. (2015). Adaptive traffic signal control for multi-intersection based on microscopic model. In Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th

International Conference on, p. 49-55.