Tracking of vehicles on nearside lanes using multiple radar sensors

Tracking overtaking vehicles is an integral aspect of Lane Change Assistant Systems. For this, in this work, a set of six radar sensors is mounted on a test vehicle. As the sensors generate spatially extended measurements from different parts of the vehicles, the estimation of position and velocity of the surrounding vehicles leads to an extended target tracking problem. This is especially the case when a vehicle is located on a nearside lane. A method for tracking these targets is proposed, under the assumption that a target vehicle comprises a random number of reflection centers located on a rectangular structure. To estimate the existence of a reflector in combination with the vehicle target state, the Joint Integrated Probabilistic Data Association Filter (JIPDA) is applied.

[1]  Gerd Wanielik,et al.  Situation Assessment for Automatic Lane-Change Maneuvers , 2010, IEEE Transactions on Intelligent Transportation Systems.

[2]  Jeffrey K. Uhlmann,et al.  An Introduction to the Combinatorics of Optimal and Approximate Data Association , 2001 .

[3]  Paul E. Rybski,et al.  Obstacle Detection and Tracking for the Urban Challenge , 2009, IEEE Transactions on Intelligent Transportation Systems.

[4]  Evangeline Pollard,et al.  Track-to-track fusion using split covariance intersection filter-information matrix filter (SCIF-IMF) for vehicle surrounding environment perception , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[5]  Christian Lundquist,et al.  Estimating the shape of targets with a PHD filter , 2011, 14th International Conference on Information Fusion.

[6]  Lennart Svensson,et al.  Adaptive Radar Sensor Model for Tracking Structured Extended Objects , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[7]  J.W. Koch,et al.  Bayesian approach to extended object and cluster tracking using random matrices , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Darko Musicki,et al.  Joint Integrated Probabilistic Data Association - JIPDA , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[9]  |Marcus Baum,et al.  Random Hypersurface Models for extended object tracking , 2009, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[10]  Gerd Wanielik,et al.  Comparison and evaluation of advanced motion models for vehicle tracking , 2008, 2008 11th International Conference on Information Fusion.

[11]  Xin Tian,et al.  Exact algorithms for four track-to-track fusion configurations: All you wanted to know but were afraid to ask , 2009, 2009 12th International Conference on Information Fusion.

[12]  Christian Lundquist,et al.  Extended Target Tracking using a Gaussian-Mixture PHD Filter , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Simon Steinmeyer,et al.  Experiences with a radar-based side assist for heavy vehicles , 2013, 2013 14th International Radar Symposium (IRS).

[14]  Bin Yang,et al.  Simulation of Automotive Radar Target Lists using a Novel Approach of Object Representation , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[15]  David G. Glynn,et al.  The permanent of a square matrix , 2010, Eur. J. Comb..

[16]  Christian Lundquist,et al.  A Gaussian mixture PHD filter for extended target tracking , 2010, 2010 13th International Conference on Information Fusion.

[17]  L. Hammarstrand,et al.  Extended Object Tracking using a Radar Resolution Model , 2012, IEEE Transactions on Aerospace and Electronic Systems.