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Authors: Yaqin Wang ; Dongfang Liu ; Hyewon Jeon ; Zhiwei Chu and Eric T. Matson

Affiliation: Department of Computer and Information Technology, Purdue University and U.S.A.

Keyword(s): Autonomous Driving, AI, Convolutional Neural Network, End-to-end Approach.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Autonomous Systems ; Computational Intelligence ; Evolutionary Computing ; Soft Computing

Abstract: End-to-end approach is one of the frequently used approaches for the autonomous driving system. In this study, we adopt the end-to-end approach because this approach has been approved to lead to a distinguished performance with a simpler system. We build a convolutional neural network (CNN) to map raw pixels from cameras of three different angles and to generate steering commands to drive a car in the Udacity simulator. Our proposed model has a promising result, which is more accurate and has lower loss rate comparing to previous models.

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Paper citation in several formats:
Wang, Y.; Liu, D.; Jeon, H.; Chu, Z. and Matson, E. (2019). End-to-end Learning Approach for Autonomous Driving: A Convolutional Neural Network Model. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-350-6; ISSN 2184-433X, SciTePress, pages 833-839. DOI: 10.5220/0007575908330839

@conference{icaart19,
author={Yaqin Wang. and Dongfang Liu. and Hyewon Jeon. and Zhiwei Chu. and Eric T. Matson.},
title={End-to-end Learning Approach for Autonomous Driving: A Convolutional Neural Network Model},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2019},
pages={833-839},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007575908330839},
isbn={978-989-758-350-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - End-to-end Learning Approach for Autonomous Driving: A Convolutional Neural Network Model
SN - 978-989-758-350-6
IS - 2184-433X
AU - Wang, Y.
AU - Liu, D.
AU - Jeon, H.
AU - Chu, Z.
AU - Matson, E.
PY - 2019
SP - 833
EP - 839
DO - 10.5220/0007575908330839
PB - SciTePress

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