  Perception of the lane boundaries is crucial for the tasks related to
autonomous trajectory control. In this paper, several methodologies for lane
detection are discussed with an experimental illustration: Hough
transformation, Blob analysis, and Bird's eye view. Following the abstraction
of lane marks from the boundary, the next approach is applying a control law
based on the perception to control steering and speed control. In the
following, a comparative analysis is made between an open-loop response, PID
control, and a neural network control law through graphical statistics. To get
the perception of the surrounding a wireless streaming camera connected to
Raspberry Pi is used. After pre-processing the signal received by the camera
the output is sent back to the Raspberry Pi that processes the input and
communicates the control to the motors through Arduino via serial
communication.
