Previous research attempted to autonomously control an internal combustion all-terrain vehicle platform but was unsuccessful due to the inability to reliably control speed. To address this, an electric motor conversion is performed on the platform to enhance its stability and controllability. A robust trail detection methodology is applied to this electric all-terrain vehicle (eATV) prototype to autonomously traverse a 3- to 5-meter-wide wooded trail, an environment that introduces unique challenges for autonomous vehicle control. The vehicle can follow a predetermined route defined by periodic waypoints placed by a human along a trail. A suite of real-time image processing algorithms is developed to respond to input from an Intel RealSense depth camera mounted on the platform. Various simultaneous solutions for path following are prioritized using a confidence scoring approach. Image processing and control techniques are also introduced for obstacle detection and avoidance schemes. The prototype vehicle has successfully navigated waypoint routes along walking trails for over 500-meters while detecting obstacles and pedestrians in real-time. To accommodate global positioning system denial that occurs occasionally in wooded environments, algorithms based on image processing are implemented for navigation toward the subsequent waypoint. An adaptive throttle-braking algorithm is also introduced to maintain a targeted velocity throughout the traversed environment. This research investigates and fulfills a budget-conscious methodology for autonomous off-road vehicle navigation along trails.