  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.
