Management of Demand and Routing in Autonomous Personal Transportation

Autonomous vehicles are becoming a reality. In particular, podcars were already launched in pilot projects. The wide use of such vehicles for personal transportation in urban areas is then just a matter of time and poses several research challenges and technical issues to the artificial intelligence and multiagent communities. This article focuses on issues related to the processing of demands for these vehicles and on routing them in an efficient way. An agent-based approach is proposed, which considers some variants for the processing of demands and for the routing. First, a centralized version is discussed. Then, extensions deal with decentralized routing, with enroute planning, intervehicle communication, and with a market-based approach where the manager of the service runs an auction to determine which customer to serve. All these variants aim at testing alternatives in order to shed light onto questions such as how the podcar service is to be provided and how trips have to be planned, as well as address challenges related to the management. Results show that, although this service can be provided by a central authority, communication, as well as reliability and fault-tolerance, should not be neglected.

[1]  Sascha Ossowski,et al.  A Computational Market for Distributed Control of Urban Road Traffic Systems , 2011, IEEE Transactions on Intelligent Transportation Systems.

[2]  Kagan Tumer,et al.  A Survey of Collectives , 2004 .

[3]  Sebastian Thrun,et al.  Anytime search in dynamic graphs , 2008, Artif. Intell..

[4]  Sarit Kraus,et al.  On the benefits of cheating by self-interested agents in vehicular networks , 2007, AAMAS '07.

[5]  Sascha Ossowski,et al.  A market-inspired approach to reservation-based urban road traffic management , 2009, AAMAS.

[6]  Peter Stone,et al.  A Multiagent Approach to Autonomous Intersection Management , 2008, J. Artif. Intell. Res..

[7]  Kagan Tumer,et al.  Collectives and Design Complex Systems , 2004 .

[8]  Ana L. C. Bazzan,et al.  Extending Traffic Simulation Based On Cellular Automata: From ParticlesTo Autonomous Agents , 2011, ECMS.

[9]  C. Flavin,et al.  Reinventing the automobile , 1995 .

[10]  필립 로빈즈 마이클,et al.  Automated vehicle control , 1989 .

[11]  Ana L. C. Bazzan,et al.  Opportunities for multiagent systems and multiagent reinforcement learning in traffic control , 2009, Autonomous Agents and Multi-Agent Systems.

[12]  Leo Liberti,et al.  Bidirectional A* Search for Time-Dependent Fast Paths , 2008, WEA.

[13]  F. Benjamin Zhan,et al.  Shortest Path Algorithms: An Evaluation Using Real Road Networks , 1998, Transp. Sci..

[14]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[15]  Judea Pearl,et al.  Heuristics : intelligent search strategies for computer problem solving , 1984 .

[16]  Andrew V. Goldberg,et al.  Computing the shortest path: A search meets graph theory , 2005, SODA '05.

[17]  William Stallings Computer Networking with Internet Protocols and Technology , 2003 .

[18]  Simon Hallé,et al.  A collaborative driving system based on multiagent modelling and simulations , 2005 .