Interpretable Autonomous Driving Model Based on Cognitive Reinforcement Learning

With the rapid development of autonomous driving technology, the safety of driving systems has increasingly become the focus of attention. However, although many existing autonomous driving decision-making algorithms, such as deep reinforcement learning, demonstrate excellent performance, their decision-making processes lack interpretability and are opaque to users. To address this problem, this paper constructs an interpretable driving model from the perspective of human cognition, which can not only imitate human driving behavior through cognitive reinforcement learning methods, but also show better performance in driving experiments. In addition, the paper also proposes an analysis method for abnormal driving behavior, which provides a new idea for discovering potential unsafe behaviors during driving and exploring the possible impact of this behavior pattern on driving tasks.