@article{ref1,
title="Generalized single-vehicle-based graph reinforcement learning for decision-making in autonomous driving",
journal="Sensors (Basel)",
year="2022",
author="Yang, Fan and Li, Xueyuan and Liu, Qi and Li, Zirui and Gao, Xin",
volume="22",
number="13",
pages="e4935-e4935",
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. <br><br>RESULTS show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22134935",
url="http://dx.doi.org/10.3390/s22134935"
}