| DECISION-MAKING FOR CONNECTED AND AUTOMATED VEHICLES
IN CHANLLENGING TRAFFIC CONDITIONS USING IMITATION AND
DEEP REINFORCEMENT LEARNING |
| Jinchao Hu , Xu Li , Weiming Hu , Qimin Xu , Yue Hu |
| Southeast University |
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| ABSTRACT |
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Decision-making is the “brain” of connected and automated vehicles (CAVs) and is vitally critical to the
safety of CAVs. The most of driving data used to train the decision-making algorithms is collected in general traffic
conditions. Existing decision-making methods are difficult to guarantee safety in challenging traffic conditions, namely
severe congestion and accident ahead. In this context, a semi-supervised decision-making algorithm is proposed to improve
the safety of CAVs in challenging traffic conditions. To be specific, we proposed the expert-generative adversarial imitation
learning (E-GAIL) that integrates imitation learning and deep reinforcement learning. The proposed E-GAIL is deployed in
roadside unit (RSU). In the first stage, the decision-making knowledge of the expert is imitated using the real-world data
collected in general traffic conditions. In the second stage, the generator of E-GAIL is further reinforced and achieves
self-learn decision-making in the simulator with challenging traffic conditions. The E-GAIL is tested in general and
challenging traffic conditions. By comparing the evaluation metrics of time to collision (TTC), deceleration to avoid a crash
(DRAC), space gap (SGAP) and time gap (TGAP), the E-GAIL greatly outperforms the state-of-the-art decision-making
algorithms. Experimental results show that the E-GAIL not only make-decision for CAVs in general traffic conditions but
also successfully enhances the safety of CAVs in challenging traffic conditions. |
| Key Words:
Connected and automated vehicles (CAVs), Traffic safety, Decision-making, Imitation learning, Deep
reinforcement learning |
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Corresponding Author.
Xu Li , Email. 
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