The Modeling of SDL Aiming at Knowledge Acquisition in Automatic Driving
This addresses the challenge of optimizing energy saving, safety, headway distance, and comfort in autonomous driving, but appears incremental as it builds on existing machine learning and fuzzy control approaches.
The paper tackles the multi-target control problem in autonomous driving by mapping multiple objective functions into a single space and introducing a Super Deep Learning (SDL) framework for optimal control based on knowledge acquisition from expert drivers. Theoretically, it claims to exceed the performance of fuzzy control methods used in automatic trains.
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.