CASIA OpenIR  > 深度强化学习团队
Model-Free Reinforcement Learning based Lateral Control for Lane Keeping
Zhang Qichao1,2; Luo Rui; Zhao Dongbin1,2; Qian Dianwei
2019
Conference NameInternational Joint Conference on Neural Networks (IJCNN)
Conference DateJuly 14-19
Conference PlaceBudapest, Hungary
Abstract

In this paper, the lateral control strategy for lane
keeping task, which is an important module in the advanced
assistant driver systems, is proposed based on the model-free
reinforcement learning. Different from the model-based methods,
our method only requires the generated data rather than the
accurate system model. Furthermore, the lateral control strategy
for driver model lane keeping is given, where driver controller
and direct yaw controller (DYC) are working at the same time to
maintain the vehicle stability. Note that the dynamic game theory
is considered for this task, where the steering wheel controller for driver and the DYC compensated controller are obtained based
on Nash game theory. Finally, we give simulation examples to
prove the validity of the proposed schemes.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26142
Collection深度强化学习团队
Affiliation1.Institute of Automation, CAS
2.University of Chinese Academy of Sciences, CAS
Recommended Citation
GB/T 7714
Zhang Qichao,Luo Rui,Zhao Dongbin,et al. Model-Free Reinforcement Learning based Lateral Control for Lane Keeping[C],2019.
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