Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning

• Main Authors: Liao, Jiangdong, Liu, Teng, Tang, Xiaolin, Mu, Xingyu, Huang, Bing, Cao, Dongpu
• Format: Journal Article
• Language: English
• Published: 16-07-2020
• Subjects: Computer Science - Learning; Computer Science - Robotics
• DOI: 10.48550/arxiv.2007.08691

Autonomous driving is a promising technology to reduce traffic accidents and
improve driving efficiency. In this work, a deep reinforcement learning
(DRL)-enabled decision-making policy is constructed for autonomous vehicles to
address the overtaking behaviors on the highway. First, a highway driving
environment is founded, wherein the ego vehicle aims to pass through the
surrounding vehicles with an efficient and safe maneuver. A hierarchical
control framework is presented to control these vehicles, which indicates the
upper-level manages the driving decisions, and the lower-level cares about the
supervision of vehicle speed and acceleration. Then, the particular DRL method
named dueling deep Q-network (DDQN) algorithm is applied to derive the highway
decision-making strategy. The exhaustive calculative procedures of deep
Q-network and DDQN algorithms are discussed and compared. Finally, a series of
estimation simulation experiments are conducted to evaluate the effectiveness
of the proposed highway decision-making policy. The advantages of the proposed
framework in convergence rate and control performance are illuminated.
Simulation results reveal that the DDQN-based overtaking policy could
accomplish highway driving tasks efficiently and safely.