A vehicle model for micro-traffic simulation in dynamic urban scenarios

In order to improve energy efficiency of transport systems, eco-driving strategies are studied world-widely. However, most literatures on eco-driving based on traditional traffic flow models, are greatly simplified, and can not evaluate the effects on detailed driving behaviors. By referring to robot motion planning approaches, in this research a microscopic vehicle model is developed and it can represent different driving behaviors, such as aggressive or conservative driving; a collision detection algorithm is proposed that takes O(1) time to check for a trajectory's collision, enabling realtime planning; and a traffic simulation system is developed by incorporating traffic rules, so that the driving behaviors such as observing or not observing traffic rules can also be represented. Experiments are conducted on the simulation platform, and the performance of different driving behaviors on travel time, mileage, comfort and eco is studied.

[1]  Jean-Paul Laumond,et al.  Robot Motion Planning and Control , 1998 .

[2]  Anthony Stentz,et al.  Optimal and efficient path planning for partially-known environments , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[3]  Kanok Boriboonsomsin,et al.  Energy and emissions impacts of a freeway-based dynamic eco-driving system , 2009 .

[4]  Paolo Fiorini,et al.  Motion Planning in Dynamic Environments Using Velocity Obstacles , 1998, Int. J. Robotics Res..

[5]  Julius Ziegler,et al.  Fast collision checking for intelligent vehicle motion planning , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[6]  Sebastian Thrun,et al.  Anytime Dynamic A*: An Anytime, Replanning Algorithm , 2005, ICAPS.

[7]  Masayoshi Tomizuka,et al.  Fast lane changing computations using polynomials , 2003, Proceedings of the 2003 American Control Conference, 2003..

[8]  Howie Choset,et al.  Principles of Robot Motion: Theory, Algorithms, and Implementation ERRATA!!!! 1 , 2007 .

[9]  Roland Siegwart,et al.  On the design of deformable input- / state-lattice graphs , 2010, 2010 IEEE International Conference on Robotics and Automation.

[10]  Dinesh Manocha,et al.  Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatio-Temporal Data , 2009, 2009 IEEE Virtual Reality Conference.

[11]  Peter Willemsen,et al.  Ribbon networks for modeling navigable paths of autonomous agents in virtual environments , 2006, IEEE Transactions on Visualization and Computer Graphics.

[12]  Dinesh Manocha,et al.  Reciprocal Velocity Obstacles for real-time multi-agent navigation , 2008, 2008 IEEE International Conference on Robotics and Automation.

[13]  Michael Schreckenberg,et al.  A cellular automaton model for freeway traffic , 1992 .

[14]  David Watts,et al.  Vehicle Performance Simulation and Optimization Including Tire Slip , 1988 .

[15]  Yoshiki Ninomiya,et al.  Local Path Planning And Motion Control For Agv In Positioning , 1989, Proceedings. IEEE/RSJ International Workshop on Intelligent Robots and Systems '. (IROS '89) 'The Autonomous Mobile Robots and Its Applications.

[16]  Dinesh Manocha,et al.  Virtualized Traffic: Reconstructing Traffic Flows from Discrete Spatio-Temporal Data , 2009, VR.

[17]  Kai Nagel,et al.  Two-lane traffic rules for cellular automata: A systematic approach , 1997, cond-mat/9712196.

[18]  Yadollah Saboohi,et al.  Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption , 2009 .

[19]  Julius Ziegler,et al.  Optimal trajectory generation for dynamic street scenarios in a Frenét Frame , 2010, 2010 IEEE International Conference on Robotics and Automation.

[20]  Peter Willemsen,et al.  Steering behaviors for autonomous vehicles in virtual environments , 2005, IEEE Proceedings. VR 2005. Virtual Reality, 2005..

[21]  Thierry Fraichard,et al.  Trajectory planning in a dynamic workspace: a 'state-time space' approach , 1998, Adv. Robotics.