Journal of Auto-vehicle Safety AssociationISSN:2005-9396(Print)한국자동차안전학회

This paper presents a motion planning algorithm of autonomous racing vehicles for mimicking the characteristics of a human driver. Time optimal maneuver of a race car has been actively studied as a major research area over the past decades. Although the time optimization problem yields a single time series solution of minimum time maneuver inputs for the vehicle, human drivers achieve similar lap times while taking various racing lines and velocity profiles. In order to model the characteristics of a specific driver and reproduce the motion, a stochastic motion planning framework based on kernelized motion primitive is introduced. The proposed framework imitates the behavior of the generated reference motion, which is based on a small number of human demonstration laps along the racetrack using Gaussian mixture model and Gaussian mixture regression. The mean and covariance of the racing line and velocity profile mimicking the driver are obtained by accumulating the outputs tested at equidistantly sampled input points. The results confirmed that the obtained lateral and longitudinal motion simulates the driver’s driving characteristics, which are feasible for actual vehicle test environments.