Lateral motion tracking of automobiles
نویسندگان
چکیده
Radar sensors, though successful in range parameters tracking, fall short in lateral characteristics tracking. On the other hand, vision systems carry out perfect estimation for lateral motion, but range parameters estimation does not surpass the performance of radar. The exact estimation of vehicle’s motion characteristics appears to be a crucial issue in modern automobile collision avoidance systems. Thus, a fusion system comprising of a radar and a FLIR could offer an overall accurate estimation for targets moving in highways. The main scope of the paper is the presentation of a double Kalman based filter which strives in lateral motion estimation mainly. The performance of the filter is tested by means of simulated data sets and compared with the results of the popular fusion method of cross-covariance matrix and the single sensor tracking for each sensor.
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