An in-vehicle tracking method using vehicular ad-hoc networks with a vision-based system

Vehicle tracking is an important issue in intelligent vehicle automation systems since it can be used to increase safety, convenience and efficiency in driving. Many of the methods for vehicle tracking use a vision-based system to recognize the neighboring vehicles and provide a real time map of nearby vehicles. In other methods, wireless communication between vehicles has been used to locate the vehicles within range and provide tracking information for driving assistance applications. In this paper we present a combination of both a vision-based system and a wireless based system to provide more accurate real-time information about neighboring vehicles. We assume that some of the vehicles are equipped with GPS receivers, a Dedicated Short Range Communication (DSRC) transceiver and one or more cameras mounted on the vehicle. This tracking method has been implemented and evaluated in urban, highway and intersection scenarios under different adoption rates. The results show that a combined approach can be more effective.

[1]  Raja Sengupta,et al.  Tracking the position of neighboring vehicles using wireless communications , 2010 .

[2]  David Beymer,et al.  A real-time computer vision system for vehicle tracking and traffic surveillance , 1998 .

[3]  Christoph Sommer A Multi-Channel IEEE 1609.4 and 802.11p EDCA Model for the Veins Framework , 2012 .

[4]  A. Varga,et al.  THE OMNET++ DISCRETE EVENT SIMULATION SYSTEM , 2003 .

[5]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[6]  Hamid Aghajan,et al.  Video-based freeway-monitoring system using recursive vehicle tracking , 1995, Electronic Imaging.

[7]  Larry S. Davis,et al.  Multiple vehicle detection and tracking in hard real-time , 1996, Proceedings of Conference on Intelligent Vehicles.

[8]  Hariharan Krishnan,et al.  Analysis of Information Dissemination in Vehicular Ad-Hoc Networks With Application to Cooperative Vehicle Safety Systems , 2011, IEEE Transactions on Vehicular Technology.

[9]  Massimo Bertozzi,et al.  Stereo vision-based vehicle detection , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[10]  Hariharan Krishnan,et al.  Adaptive intervehicle communication control for cooperative safety systems , 2010, IEEE Network.

[11]  Victor C. M. Leung,et al.  Connectivity-aware minimum-delay geographic routing with vehicle tracking in VANETs , 2011, Ad Hoc Networks.

[12]  Jitendra Malik,et al.  A real-time computer vision system for measuring traffic parameters , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Falko Dressler,et al.  On the Necessity of Accurate IEEE 802.11P Models for IVC Protocol Simulation , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[14]  Christoph Schroth,et al.  Simulation of car-to-car messaging: analyzing the impact on road traffic , 2005, 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems.

[15]  Daniel Krajzewicz,et al.  SUMO - Simulation of Urban MObility An Overview , 2011 .

[16]  Martin V. Butz,et al.  Improved tracking and behavior anticipation by combining street map information with Bayesian-filtering , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[17]  Reinhard German,et al.  Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis , 2011, IEEE Transactions on Mobile Computing.

[18]  Andry Rakotonirainy,et al.  Simulation architecture for the design of Cooperative Collision Warning systems , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.