Abstract:

Aiming at the common parallel parking scene， a novel parallel parking path planning method for vehicle is proposed based on adaptive neuro?fuzzy inference system. With the parking path obtained from parallel parking path planning based on optimization algorithm as training sample， Python script is used to build the automated training framework with adaptive genetic algorithm and quasi?Newton algorithm as its core for enabling the adaptive neuro?fuzzy inference system automatically trained can not only inherit the merits of wider application scope of parallel parking path planning method based on optimization algorithm， but also effectively get rid of enormous computation efforts. The feasibility and effectiveness of the proposed method are verified by simulation， and the results show that the adaptive neuro?fuzzy inference system automatically trained can quickly plan the feasible parallel parking path based on the initial parking posture of vehicle and parking space information.

Key words: parallel parking path planning, adaptive neuro?fuzzy inference system, adaptive genetic algorithm, quasi?Newton algorithm, automated training framework