Automatic Vehicle Model Recognition and Lateral Position Estimation Based on Magnetic Sensors

Abstract

This paper presents a new approach for automatic vehicle model recognition and simultaneous estimation of lateral transit position, based on magnetic sensor technology. A set of magnetic sensors is deployed on the road surface and, upon transit of a target vehicle on the equipment, the system records six magnetic signatures relative to different vehicle sections. The recorded signatures are then compared with the Dynamic Time Warping algorithm to previously recorded ones, which are relative to known vehicles that have transited at known lateral position; the system then assesses whether the target vehicle’s model matches one of the models already in the database, and estimates its lateral transit position. With the considered experimental set-up, the system is able to discriminate between many different vehicle models and six lateral positions, with a resolution of about $20\,cm$ : the performance of the system is presented by comparing a set of different classifiers. In terms of vehicle model recognition, 1-Nearest Neighbor classifier obtains 0% of misclassification rate, while for lateral position estimation, if an error of one position is tolerated (precision of $\pm \,20\,cm$ ), the system is shown to reach 2.4% of misclassification rate.