This paper presents the vision and path planning software design of a totally autonomous vehicle built to compete in the 13th Intelligent Ground Vehicle Competition, IGVC. The vehicle, Calculon, is based on a powered wheelchair and uses a variety of sensors for its navigation and obstacle avoidance including a 3CCD Sony color camera, an outdoor laser range finder, a DGPS, 2 wheel encoders and a solid state compass. The modules forming the core vision system include: filters, color classifier, image segments, and line finder. Our color classifier is based on a modified implementation of an adaptive Gaussian color model similar to those used in some skin detection algorithms. A combination of various image enhancing filters and the color classifier allow for the isolation of possible obstacles within the image. After filtering the image for areas of high brightness and contrast, the line finder performs a Hough Transform to find lines in the image. Our path planning is accomplished using a variety of known and custom algorithms in combination including a modified road map method, a Rapidly Exploring Trees method and a Gaussian Potential Field's method. This paper will present the software design and methods of our autonomous vehicle focusing mainly on the 2 most difficult components, the vision and path planning.