Citation

Liang Y, Gu Z, Zhang Z. Comput. Intell. Neurosci. 2022; 2022: e8928632.

Copyright

(Copyright © 2022, Hindawi Publishing)

DOI

10.1155/2022/8928632

PMID

35437438

PMCID

PMC9013314

Abstract

In this paper, we proposed a new theory of solving the multitarget control problem by introducing a machine learning framework in automatic driving and implementing the acquisition of excellent drivers' knowledge. Nowadays, there still exist some core problems that have not been fully realized by the researchers in automatic driving, such as the optimal way to control the multitarget objective functions of energy saving, safe driving, headway distance control, and comfort driving. It is also challenging to resolve the networks that automatic driving is relied on and to improve the performance of GPU chips on complex driving environments. According to these problems, we developed a new theory to map multitarget objective functions in different spaces into the same one and thus introduced a machine learning framework of SDL (super deep learning) for optimal multitarget control based on knowledge acquisition. We will present in this paper the optimal multitarget control by combining the fuzzy relationship of each multitarget objective function and the implementation of excellent drivers' knowledge acquired by machine learning. Theoretically, the impact of this method will exceed that of the fuzzy control method used in the automatic train.

Language: en

Keywords

Accidents, Traffic; Machine Learning; *Automobile Driving; *Deep Learning