Resumen
Lane-changing is an important operation of an autonomous vehicle driving on the road. Safety and comfort are fully considered by excellent drivers in lane-changing operation. However, only the kinematic and dynamic constraints are taken into account in the traditional path planning methods, and the path generated by the traditional methods is very different from the actual trajectory of the vehicle driven by the excellent driver. In this paper, a path planning method for imitating the lane-changing operation of excellent drivers is proposed. Five experienced drivers are invited to do the lane-changing test, and the lane-changing trajectories data under different conditions are recorded. The excellent driver lane-changing model is established based on the genetic algorithm (GA) and back propagation (BP) neural network trained by the data of the lane-changing tests. The proposed approach can plan out an optimized lane change path according to the vehicle condition by learning the excellent drivers’ driving routes. The results of simulations verify that the path generated by the proposed algorithm is basically same as the track selected by the excellent drivers under same conditions, which can reflect the characteristics of the operations of the excellent driver. While applying safe lane-changing to autonomous vehicle, it can improve the ride comfort of the vehicle and therefore reduce the probability of motion sickness of the passengers caused by improper operation during lane change.