Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving.
Sensors (Basel)
; 22(13)2022 Jun 29.
Article
em En
| MEDLINE
| ID: mdl-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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Condução de Veículo
/
Redes Neurais de Computação
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article