关键词: 自动驾驶, 闭环学习, 弹性权重巩固, 安全关键场景

Abstract:

To build a highly secure and trustworthy autonomous driving system in an open environment， this paper proposes a closed-loop learning method for autonomous driving decision-making algorithms in response to the long-tail distribution problem in autonomous driving scenarios. This method achieves algorithmic closed-loop through the generation of safety-critical scenarios and continuous learning. Firstly， for the basic algorithm that performs well in common driving scenarios， safety-critical scenarios with threat are generated to identify algorithmic flaws. Secondly， a continuous learning method that combines elastic weight consolidation and linear multi-strategy heads is adopted to further train the self-vehicle algorithm in safety-critical scenarios， avoiding the problem of catastrophic forgetting. Finally， the algorithm's adaptability to scenarios is enhanced through multiple closed-loop iteration. This paper takes the soft actor-critic algorithm as the basic algorithm to verify the effectiveness of the proposed closed-loop learning method. After two rounds of closed-loop iterative tests with significant environmental differences and continuously increasing difficulty， the collision rates of the two baseline methods without continuous learning strategy and only using experience replay strategy， and the method proposed in this paper are 25.40%， 25.33%， and 14.43% respectively. The comparison results show that the method proposed in this paper has a stronger comprehensive ability to resist catastrophic forgetting and explore new tasks. Therefore， the proposed closed-loop learning method can effectively improve the scene adaptability of learning-based autonomous driving decision-making algorithms and achieve iterative optimization of the algorithms.

Key words: autonomous driving, closed-loop learning, elastic weight consolidation, safety-critical scenarios