Variational Autoencoder for End-to-End Control of Autonomous Driving with Novelty Detection and Training De-biasing
This paper introduces a new method for end-to-end training of deep neural networks (DNNs) and evaluates it in the context of autonomous driving. DNN training has been shown to result in high accuracy for perception to action learning given sufficient training data. However, the trained models may fa...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/1721.1/118139 https://orcid.org/0000-0002-9334-1706 https://orcid.org/0000-0002-2225-7275 https://orcid.org/0000-0001-5473-3566 |