An adaptive longitudinal control method for autonomous follow driving based on neural dynamic programming and internal model structure

Autonomous vehicles are considered to have great potentials in improving transportation safety and efficiency. Autonomous follow driving is one of the highly probable application forms of autonomous vehicles in the near future. In this article, we aim at the basic autonomous following form with one follower and one leader. Proper longitudinal regulation of the follower vehicle is essential for the driving quality of the two-vehicle platoon. Focusing on this problem, a novel longitudinal control method composing of a learning-based acceleration decision phase and an internal model–based acceleration tracking phase is proposed for the follower vehicle. In the acceleration decision phase, proper acceleration commands of the follower that adjusts the following distance converging to the target value are determined by a near-optimal acceleration policy which is obtained through an online reinforcement learning algorithm named neural dynamic programming. In the acceleration tracking phase, throttle and brake control commands that drive the vehicle as the decided acceleration are derived by an internal model control structure. The performance of our proposed method is verified by simulation experiments conducted with CarSim, an industry recognized vehicle dynamic simulator.

Abstract licence: CC BY

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