Markov-Based Lane Positioning Using Intervehicle Communication

The majority of today's navigation techniques for intelligent transportation systems use global positioning systems (GPS) that can provide position information with bounded errors. However, due to the low accuracy that is experienced with standard GPS, it is difficult to determine a vehicle's position at lane level. Using a Markov-based approach based on sharing information among a group of vehicles that are traveling within communication range, the lane positions of vehicles can be found. The algorithm's effectiveness is shown in both simulations and experiments with real data.

[1]  Jin Wang,et al.  Lane keeping based on location technology , 2005, IEEE Transactions on Intelligent Transportation Systems.

[2]  M. A. Do,et al.  Camera detection, classification, and positioning of vehicles on a multi-lane road , 1993, Proceedings of IEEE Singapore International Conference on Networks/International Conference on Information Engineering '93.

[3]  H. T. Kung,et al.  Ad hoc relay wireless networks over moving vehicles on highways , 2001, MobiHoc '01.

[4]  Sebastian Thrun,et al.  Particle Filters in Robotics , 2002, UAI.

[5]  B. A. Shenoi,et al.  Introduction to Digital Signal Processing and Filter Design , 2005 .

[6]  Marco Roccetti,et al.  How Do You Quickly Choreograph Inter-Vehicular Communications? A Fast Vehicle-to-Vehicle Multi-Hop Broadcast Algorithm, Explained , 2007, 2007 4th IEEE Consumer Communications and Networking Conference.

[7]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[8]  D. Gruyer,et al.  A new multi-lanes detection using multi-camera for robust vehicle location , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[9]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[10]  Mohan M. Trivedi,et al.  Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation , 2006, IEEE Transactions on Intelligent Transportation Systems.

[11]  W. Enkelmann.  FleetNet - applications for inter-vehicle communication , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[12]  Sheng-Fuu Lin,et al.  Lane detection using color-based segmentation , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[13]  Chen Yanhua,et al.  Approach to lane departure detection , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[14]  M.M. Artimy,et al.  Connectivity in inter-vehicle ad hoc networks , 2004, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513).

[15]  Dieter Fox,et al.  Markov localization - a probabilistic framework for mobile robot localization and navigation , 1998 .

[16]  Pierre-Yves Gilliéron,et al.  Enhanced Navigation System for Road Telematics , 2003 .

[17]  N. H. C. Yung,et al.  Lane detection by orientation and length discrimination , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[18]  M. Barth,et al.  Lane-level positioning for in-vehicle navigation and automated vehicle location (AVL) systems , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[19]  I. Reading,et al.  Adaptive lane finding in road traffic image analysis , 1994 .

[20]  Azim Eskandarian,et al.  Performance characteristics of inter-vehicle ad hoc networks , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[21]  Mohinder S. Grewal,et al.  Global Positioning Systems, Inertial Navigation, and Integration , 2000 .