Engineering Commons^™

Autonomous Driving With A Simulation Trained Convolutional Neural Network, Cameron Franke

University of the Pacific Theses and Dissertations

Autonomous vehicles will help society if they can easily support a broad range of driving environments, conditions, and vehicles.

Achieving this requires reducing the complexity of the algorithmic system, easing the collection of training data, and verifying operation using real-world experiments. Our work addresses these issues by utilizing a reflexive neural network that translates images into steering and throttle commands. This network is trained using simulation data from Grand Theft Auto V~\cite{gtav}, which we augment to reduce the number of simulation hours driven. We then validate our work using a RC car system through numerous tests. Our system successfully drive …