Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles
Year: 2025
Type: conference-paper
Abstract: The safe overtaking of autonomous vehicles has become an important focus in recent robotics and AI research. Considering the scenario of overtaking with oncoming vehicles, this paper proposes a hierarchical framework based on deep reinforcement learning to interact with the traffic environment and learn to overtake safely and efficiently. At the high level, an optimized actor-critic network with TRPO deep reinforcement learning algorithm is used to make safe overtaking decisions. At the low level, a reliable lane-changing path planning strategy is employed for motion control. A well-designed reward function is introduced to guide the agent in learning efficient overtaking behaviors. The effectiveness of the proposed framework is demonstrated through simulation experiments.
Authors: Ailipaer Aihaiti, Farshad Arvin, Junyan Hu
Institution: Durham University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409660543