Automated driving is one of the main trends in automotive field. However, existing algorithms for automated driving are mostly designed based on statistical rules without considering the onboard drivers' individual features, which may lead to human-vehicle inconsistency during driving. In order to meet the driving preferences of any individual driver rather than classify into several driving styles (e.g. aggressive, conservative, moderate), this paper proposes a policy-learning method based on generative adversarial imitation learning. Specifically, an imitation learning framework consisting of generators and discriminators is built to train a policy network. Then, expert models are established to acquire driving data based on characteristics of individual drivers. In addition, the feasibility of the method is verified under scenarios of car following and lane changing. Finally, the generalization ability of the method is verified compared to the behavioral cloning method. The results reveal that the proposed method is capable to mimic the personalized features of human drivers.
Personalized Decision-Making and Control for Automated Vehicles Based on Generative Adversarial Imitation Learning
2023-09-24
3204282 byte
Conference paper
Electronic Resource
English
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