  Modern vehicles communicate data to and from sensors, actuators, and
electronic control units (ECUs) using Controller Area Network (CAN) bus, which
operates on differential signaling. An autonomous ECU responsible for the
execution of decision commands to an autonomous vehicle is developed by
assimilating the information from the CAN bus. The conventional way of parsing
the decision commands is motion planning, which uses a path tracking algorithm
to evaluate the decision commands. This study focuses on designing a robust
controller using behavioral cloning and motion planning of autonomous vehicle
using a deep learning framework. In the first part of this study, we explore
the pipeline of parsing decision commands from the path tracking algorithm to
the controller and proposed a neural network-based controller ($N^2C$) using
behavioral cloning. The proposed network predicts throttle, brake, and torque
when trained with the manual driving data acquired from the CAN bus. The
efficacy of the proposed method is demonstrated by comparing the accuracy with
the Proportional-Derivative-Integral (PID) controller in conjunction with the
path tracking algorithm (pure pursuit and model predictive control based path
follower). The second part of this study complements $N^2C$, in which an
end-to-end neural network for predicting the speed and steering angle is
proposed with image data as an input. The performance of the proposed
frameworks are evaluated in real-time and on the Udacity dataset, showing
better metric scores in the former and reliable prediction in the later case
when compared with the state-of-the-art methods.
