Driving decision making of autonomous vehicles based on deep reinforcement learning with prefrontal cortex-like cognition

Abstract

Safe and efficient decision-making of autonomous vehicles in complex dynamic scenarios requires a decision-making system with human-like cognitive ability, however, existing deep reinforcement learning methods suffer from insufficient generalization ability of unknown scenarios and exploration-exploitation imbalance. In order to solve the above problems, this study proposes a novel prefrontal cortex (PFC) decision-making model, which builds a multi-module synergistic cognitive architecture by modeling the spatio-temporal reasoning of the lateral prefrontal cortex (LPFC), the reward prediction of the medial prefrontal cortex (MPFC), and the adaptive adjustment function of the anterior cingulate cortex (ACC). The innovations are fusion of graph convolutional network (GCN) and long short-term memory network (LSTM) to capture vehicle interaction features; introduction of unsupervised clustering and deep belief network (DBN) to achieve metacognitive planning of action-reward causal association, proposing a dynamic exploration rate regulation mechanism based on alertness, and realizing strategy optimization in complex scenarios through dopamine-based reward prediction error. In this study, this study test the performance of the method in highway and ring intersection scenarios and compare it with existing deep reinforcement learning (DRL) and graph reinforcement learning (GRL) methods. The experimental results show that the PFC model can perform spatio-temporal and task reasoning, and is able to make better decisions in complex and changing scenarios, which significantly improves the efficiency of access. This study can provide reference value for the development of biological neural models and promote their application in dynamic traffic interaction scenarios.