Decision-Making for Autonomous Driving via a Coupled Reinforcement Learning Network Combined With Risk Assessment

Abstract

The realization of autonomous driving(AV) is closely linked to the development of intelligent decision-making modules that can operate safely in dynamic, uncertain environments. To address issues such as delayed response and poor coupling in highway scenarios, this paper proposes a hierarchical Coupled Decision-Making (CDM) framework. The CDM framework separates high-level intention planning from low-level behavior control. The upper layer uses DE-D3QN, enhanced with episodic memory buffer (EMB) and experience replay buffer (ERB), to improve learning efficiency in large-scale state spaces. The lower layer employs BF-TD3 with a barrier function to generate continuous, risk-aware control actions. Additionally, a probabilistic risk model combining lane speed gain, risk indicators, and Bayesian estimation enables adaptive evaluation of surrounding vehicle impact. To verify the contribution of each component, ablation studies are conducted on the virtual distance model and barrier function. Results show that CDM achieves better training stability, decision robustness, and a more favorable balance between risk and efficiency compared to baselines, while aligning decisions with driving expectations.