Controlling ATV Steering Angle on a Forest Path Using Machine Learning

This paper discusses research to control the steering wheel angle of an All-Terrain Vehicle (ATV) in a forest environment using machine learning techniques and a camera feed. The research uses different semantic segmentation (S.S.) techniques to analyze visual data gathered from the ATV driven in the forest. It then uses different linear regression models to find the best suited model to predict the steering wheel angle. This paper discusses the data gathering process for the visual data and steering wheel angles. It also discusses the data preparation for the S.S. models and the S.S. models used. Then it presents the data preparation for the linear regression models used after the S.S.. Finally, the training and usage of the different linear regression models, the final results, and conclusions are represented. All models were computed offline on a Jetson Nano Nvidia GPU. Our objective is to find the best model which will allow the vehicle to drive in real-time in future trials.