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.

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