Citation

Wang X, Wu C, Xue J, Chen Z. Information (Basel) 2020; 11(6): e295.

Copyright

(Copyright © 2020, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/info11060295

PMID

unavailable

Abstract

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.

Language: en

Keywords

data visualization; deep reinforcement learning; driving decision; human-like; personalization; smart car