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.
| Identifer | oai:union.ndltd.org:pacific.edu/oai:scholarlycommons.pacific.edu:uop_etds-3970 |
| Date | 01 January 2017 |
| Creators | Franke, Cameron |
| Publisher | Scholarly Commons |
| Source Sets | University of the Pacific |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of the Pacific Theses and Dissertations |
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