Enhancing Autonomous Driving through Dual-Process Learning with Behavior and Reflection Integration

Abstract

Contemporary autonomous driving (AD) methodologies, which predominantly convert visual features into control directives, face long-tail challenges due to constraints imposed by limited data distribution. Conversely, human drivers exhibit proficiency in such conditions, underscoring the significance of emulating human cognition in AD systems. Therefore, we introduce Dual-Process Learning (D-PL) approach for cognitive-enhanced decision-making. Inspired by dual-process theory, the D-PL method combines Behavior Pattern Learning (BPL) and Self-Reflective Learning (SRL) to integrate quick, intuitive decisions with deliberate, analytical reasoning, constructing a hierarchical decision model for sophisticated trajectory planning. Our approach improves decision-making, enhances adaptability, and tackles the crucial open-world generalization challenge encountered by current AD methods. Comprehensive evaluations on the nuScenes dataset validate the robustness of our method, demonstrating its superior performance in navigating the intricacies of real-world contrasting with conventional models.