Citation

Yang F, Li X, Liu Q, Li Z, Gao X. Sensors (Basel) 2022; 22(13): e4935.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s22134935

PMID

35808428

Abstract

In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the interactive information between agents in the environment into the decision-making process, this paper proposes a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL algorithm). The SGRL algorithm introduces graph convolution into the traditional deep neural network (DQN) algorithm, adopts the training method for a single agent, designs a more explicit incentive reward function, and significantly improves the dimension of the action space. The SGRL algorithm is compared with the traditional DQN algorithm (NGRL) and the multi-agent training algorithm (MGRL) in the highway ramp scenario.

RESULTS show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency.

Language: en

Keywords

autonomous driving; decision-making; deep reinforcement learning; graph convolution