End-to-end Learning Approach for Autonomous Driving: A Convolutional Neural Network Model

Yaqin Wang, Dongfang Liu, Hyewon Jeon, Zhiwei Chu, Eric Matson

2019

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 Harvard Style

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, pages 833-839. DOI: 10.5220/0007575908330839


in Bibtex Style

@conference{icaart19,
author={Yaqin Wang and Dongfang Liu and Hyewon Jeon and Zhiwei Chu and Eric 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},
}


in EndNote Style

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
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