Deep reinforcement learning for decision making of autonomous vehicle in non-lane-based traffic environments.

ABSTRACT: Existing research on decision-making of autonomous vehicles (AVs) has mainly focused on normal road sections, with limited exploration of decision-making in complex traffic environments without lane markings. Taking toll plaza diverging area as an example, this study proposes a lateral motion strategy for AVs based on deep reinforcement learning (DRL) algorithms. First, a microscopic simulation platform is developed to simulate the realistic diverging trajectories of human-driven vehicles (HVs), providing AVs with a high-fidelity training environment. Next, a DRL-based self-efficient lateral motion strategy for AVs is proposed, with state and reward functions tailored to the environmental features of the diverging area. Simulation results indicate that the strategy can significantly reduce

SUBMITTER: Fei Y 

PROVIDER: S-EPMC12002507 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature