Causal reasoning decision-making for vehicle longitudinal automatic driving

被引：0
|
作者：
Gao Z.-H. [1 ]
Sun T.-J. [1 ]
He L. [1 ]
机构：
[1] State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2019年 / 49卷 / 05期
关键词：
Automatic driving; Decision-making algorithm; Markov decision process; Reinforcement Q learning; Vehicle engineering;
D O I：
10.13229/j.cnki.jdxbgxb20190078
中图分类号：
学科分类号：
摘要：
In order to solve cause-and-effect problems during decision-making, in this paper, the Markov Decision Process (MDP) model is established by the analysis of car-following at first. Then, the state set and the action set are designed through the combination of driving simulator experimental data and driving risk principle. Third, the reward functions are designed according to different driving states. Finally, a causal reasoning mechanism during the process of decision-making is proposed and reinforcement Q-learning algorithm is applied to solve the MDP model. The feasibility and effectiveness of the proposed method are verified through the simulation tests with random driving conditions. © 2019, Jilin University Press. All right reserved.
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页码：1392 / 1404
页数：12
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