Experiments in Autonomous Driving Through Imitation Learning
This paper addresses the problem of autonomous driving for researchers, but the limited effectiveness due to data imbalance suggests an incremental contribution.
This report explores methods for creating a self-driving vehicle using supervised learning and an RGBD camera. Despite exploring various approaches, an unbalanced dataset limited the effectiveness of the imitation learning methods.
This report demonstrates several methods used to make a self-driving vehicle using a supervised learning algorithm and a forward-facing RGBD camera. The project originally involved research in creating an adversarial attack on the vehicle's model, but due to difficulties with the initial training of the car, the plans were discarded in favor of completing the imitation learning portion of the project. Many approaches were explored, but due to challenges introduced by an unbalanced data set, the approaches had limited effectiveness.