Journal of Bionic Engineering ›› 2021, Vol. 18 ›› Issue (6): 1439-1451.doi: 10.1007/s42235-021-00113-9
Brain-like Intelligent Decision-making Based on Basal Ganglia and Its Application in Automatic Car-following
Tianjun Sun 1,2, Zhenhai Gao 1,2, Zhiyong Chang 3, Kehan Zhao 4
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1 State Key Laboratory of Automotive Simulation
and Control , Jilin University , Changchun 130022 , China
2 College of Automotive Engineering , Jilin University , Changchun 130022 , China
3 College of Biological and Agricultural Engineering , Jilin University , Changchun 130022 , China
4 College of Materials Science and Engineering , Jilin University , Changchun 130022 , China
Brain-like Intelligent Decision-making Based on Basal Ganglia and Its Application in Automatic Car-following
Tianjun Sun 1,2, Zhenhai Gao 1,2, Zhiyong Chang 3, Kehan Zhao 4
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1 State Key Laboratory of Automotive Simulation and Control , Jilin University , Changchun 130022 , China
2 College of Automotive Engineering , Jilin University , Changchun 130022 , China
3 College of Biological and Agricultural Engineering , Jilin University , Changchun 130022 , China
4 College of Materials Science and Engineering , Jilin University , Changchun 130022 , China
摘要： The anthropomorphic intelligence of autonomous driving has been a research hotspot in the world. However, current studies have not been able to reveal the mechanism of drivers' natural driving behaviors. Therefore, this thesis starts from the perspective of cognitive decision-making in the human brain, which is inspired by the regulation of dopamine feedback in the basal ganglia, and a reinforcement learning model is established to solve the brain-like intelligent decision-making problems in the process of interacting with the environment. In this thesis, first, a detailed bionic mechanism architecture based on basal ganglia was proposed by the consideration and analysis of its feedback regulation mechanism; second, the above mechanism was transformed into a reinforcement Q -learning model, so as to implement the learning and adaptation abilities of an intelligent vehicle for brain-like intelligent decision-making during car-following; finally, the feasibility and effectiveness of the proposed method were verified by the simulations and real vehicle tests.