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

A. Amodio, M. Ermidoro, S.M. Savaresi, F.Previdi

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
20cm: 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
±20cm), the
system is shown to reach
2:4% of misclassification rate.

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