Autonomous Vehicle Control using Image Processing

Abstract

This thesis describes the design of an inexpensive autonomous vehicle system using a small scaled model vehicle. The system is capable of operating in two different modes: telerobotic manual mode and automated driving mode.

In telerobotic manual mode, the model vehicle is controlled by a human driver at a stationary remote control station with full-scale steering wheel and gas pedal. The vehicle can either be an unmodified toy remote-control car or a vehicle equipped with wireless radio modem for communication and microcontroller for speed control. In both cases the vehicle also carries a video camera capable of transmitting video images back to the remote control station where they are displayed on a monitor.

In automated driving mode, the vehicle's lateral movement is controlled by a lateral control algorithm. The objective of this algorithm is to keep the vehicle in the center of a road. Position and orientation of the vehicle are determined by an image processing algorithm identifying a white middle marker on the road. Two different algorithm for image processing have been designed: one based on the pixel intensity profile and the other on vanishing points in the image plane. For the control algorithm itself, two designs are introduced as well: a simple classical P-control and a control scheme based on H-Infinity.

The design and testing of this autonomous vehicle system are performed in the Flexible Low-cost Automated Scaled Highway (FLASH) laboratory at Virginia Tech.