Autonomous Driving with a Simulation Trained Convolutional Neural Network

Author

Cameron Franke, University of the PacificFollow

Date of Award

2017

Document Type

Thesis

Degree Name

Master of Science (M.S.)

Department

Engineering

First Advisor

Elizabeth Basha

First Committee Member

Michael Doherty

Second Committee Member

Chadi El Kari

Abstract

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 98 of 100 laps of a track with multiple road types and difficult turns; it also successfully avoids collisions with another vehicle in 90\% of the trials.

Recommended Citation

Franke, Cameron. (2017). Autonomous Driving with a Simulation Trained Convolutional Neural Network. University of the Pacific, Thesis. https://scholarlycommons.pacific.edu/uop_etds/2971