TY - JOUR
T1 - A method of personalized driving decision for smart car based on deep reinforcement learning
AU - Wang, Xinpeng
AU - Wu, Chaozhong
AU - Xue, Jie
AU - Chen, Zhijun
PY - 2020
Y1 - 2020
N2 - To date, automatic driving technology has become a hotspot in academia. It is necessary to provide a personalization of automatic driving decision for each passenger. The purpose of this paper is to propose a self-learning method for personalized driving decisions. First, collect and analyze driving data from different drivers to set learning goals. Then, Deep Deterministic Policy Gradient algorithm is utilized to design a driving decision system. Furthermore, personalized factors are introduced for some observed parameters to build a personalized driving decision model. Finally, compare the proposed method with classic Deep Reinforcement Learning algorithms. The results show that the performance of the personalized driving decision model is better than the classic algorithms, and it is similar to the manual driving situation. Therefore, the proposed model can effectively learn the human-like personalized driving decisions of different drivers for structured road. Based on this model, the smart car can accomplish personalized driving.
AB - To date, automatic driving technology has become a hotspot in academia. It is necessary to provide a personalization of automatic driving decision for each passenger. The purpose of this paper is to propose a self-learning method for personalized driving decisions. First, collect and analyze driving data from different drivers to set learning goals. Then, Deep Deterministic Policy Gradient algorithm is utilized to design a driving decision system. Furthermore, personalized factors are introduced for some observed parameters to build a personalized driving decision model. Finally, compare the proposed method with classic Deep Reinforcement Learning algorithms. The results show that the performance of the personalized driving decision model is better than the classic algorithms, and it is similar to the manual driving situation. Therefore, the proposed model can effectively learn the human-like personalized driving decisions of different drivers for structured road. Based on this model, the smart car can accomplish personalized driving.
KW - Data visualization
KW - Deep reinforcement learning
KW - Driving decision
KW - Human-like
KW - Personalization
KW - Smart car
UR - http://www.scopus.com/inward/record.url?scp=85087452377&partnerID=8YFLogxK
U2 - 10.3390/INFO11060295
DO - 10.3390/INFO11060295
M3 - Article
AN - SCOPUS:85087452377
SN - 2078-2489
VL - 11
JO - Information (Switzerland)
JF - Information (Switzerland)
IS - 6
M1 - 295
ER -