An automated driving strategy generating method based on WGAIL-DDPG - Zhabg, Mingheng; Wan, Xing; Gang, Longhui; Lv, Xinfei; Wu, Zengwen; Liu, Zhaoyang (Zielona Góra: Uniwersytet Zielonogórski)
Reliability, efficiency and generalization are basic evaluation criteria for a vehicle automated driving system. This paper proposes an automated driving decision-making method based on the Wasserstein generative adversarial imitation learning-deep deterministic policy gradient (WGAIL-DDPG). Here the exact reward function is designed based on the requirements of a vehicle`s driving performance, i.e., safety, dynamic and ride comfort performance.
Keywords: automated driving system; deep learning; deep reinforcement learning; imitation learning; deep deterministic policy gradient