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[2021-Vol.18-Issue 6]Brain-like Intelligent Decision-making Based on Basal Ganglia and Its Application in Automatic Car-following
Post: 2021-12-23 14:04  View:432

Journal of Bionic Engineering (2021) 18:1439–1451  https://doi.org/10.1007/s42235-021-00113-9

Brain?like Intelligent Decision-making Based on Basal Ganglia and Its Application in Automatic Car?following 

Tianjun Sun1,2  · Zhenhai Gao1,2 · Zhiyong Chang3  · Kehan Zhao4

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 

Abstract  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, frst, 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; fnally, the feasibility and efectiveness of the proposed method were verifed by the simulations and real vehicle tests. 

Keywords  Brain-like intelligent decision-making · Dopamine in basal ganglia · Reinforcement learning · Longitudinal autonomous driving

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The interaction process of reinforcement learning

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