The Modeling of SDL Aiming at Knowledge Acquisition in Automatic Driving

TL;DR

This paper introduces SDL, a machine learning framework that models multi-target control in automatic driving by integrating expert driver knowledge and fuzzy relationships to improve energy efficiency, safety, and comfort.

Contribution

It presents a novel theory mapping multi-target objectives into a unified space and implementing SDL for optimal control, advancing knowledge acquisition in autonomous driving.

Findings

Enhanced multi-target control accuracy

Improved energy efficiency and safety metrics

Theoretical superiority over fuzzy control methods

Abstract

In this paper we proposed an ultimate theory to solve the multi-target control problem through its introduction to the machine learning framework in automatic driving, which explored the implementation of excellent drivers' knowledge acquisition. Nowadays there exist some core problems that have not been fully realized by the researchers in automatic driving, such as the optimal way to control the multi-target objective functions of energy saving, safe driving, headway distance control and comfort driving, as well as the resolvability of the networks that automatic driving relied on and the high-performance chips like GPU on the 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 multi-targetcontrol based on knowledge acquisition. We will present in this paper the optimal multi-target control by combining the fuzzy relationship of each multi-target 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 automatic train.