Autonomous Vehicle Control Using a Deep Neural Network and Jetson Nano

Rocco Febbo; Brendan Flood; Julian Halloy; Patrick Lau; Kwai Wong; Alan Ayala

doi:10.1145/3311790.3396669

Autonomous Vehicle Control Using a Deep Neural Network and Jetson Nano

Rocco Febbo, Brendan Flood, Julian Halloy, Patrick Lau, Kwai Wong, Alan Ayala

City University of Hong Kong

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

8 Citations (Scopus)

Abstract

The idea of a self-driving car is one which is actively studied and tested for use on the road. In addition, the machine learning tools required to create such a vehicle has become more and more available to the public as time goes on. With a number of different libraries and softwares available for free download to design and train neural networks and with affordable but powerful miniature computers on the market, one can explore the possibility of creating a self-driving vehicle. The goal of our project was to construct such a car on a small scale using parts and software that are accessible to anyone on an affordable budget ($250), and to test the effectiveness of DNN software neural networks on training such a car. This project serves as a simple testbed for experimenting different ideas in self driving vehicle. Core ideas of autonomous vehicles are explored with machine learning in mind. The paper details the challenges and experience of project and is the result of an REU project support by the NSF.

Original language	English
Title of host publication	PEARC '20
Subtitle of host publication	Practice and Experience in Advanced Research Computing
Publisher	Association for Computing Machinery
Pages	333-338
ISBN (Print)	978-1-4503-6689-2
DOIs	https://doi.org/10.1145/3311790.3396669
Publication status	Published - 26 Jul 2020
Event	2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020 - Virtual, Online, United States Duration: 27 Jul 2020 → 31 Jul 2020 https://pearc.acm.org/pearc20

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020
Country/Territory	United States
City	Virtual, Online
Period	27/07/20 → 31/07/20
Internet address	https://pearc.acm.org/pearc20

Research Keywords

autonomous vehicle
deep learning
edge computing
neural networks

Access Output

10.1145/3311790.3396669

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{8ef991368df548df82e8dc6a092fde3e,

title = "Autonomous Vehicle Control Using a Deep Neural Network and Jetson Nano",

abstract = "The idea of a self-driving car is one which is actively studied and tested for use on the road. In addition, the machine learning tools required to create such a vehicle has become more and more available to the public as time goes on. With a number of different libraries and softwares available for free download to design and train neural networks and with affordable but powerful miniature computers on the market, one can explore the possibility of creating a self-driving vehicle. The goal of our project was to construct such a car on a small scale using parts and software that are accessible to anyone on an affordable budget ($250), and to test the effectiveness of DNN software neural networks on training such a car. This project serves as a simple testbed for experimenting different ideas in self driving vehicle. Core ideas of autonomous vehicles are explored with machine learning in mind. The paper details the challenges and experience of project and is the result of an REU project support by the NSF.",

keywords = "autonomous vehicle, deep learning, edge computing, neural networks, autonomous vehicle, deep learning, edge computing, neural networks, autonomous vehicle, deep learning, edge computing, neural networks",

author = "Rocco Febbo and Brendan Flood and Julian Halloy and Patrick Lau and Kwai Wong and Alan Ayala",

year = "2020",

month = jul,

day = "26",

doi = "10.1145/3311790.3396669",

language = "English",

isbn = "978-1-4503-6689-2",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "333--338",

booktitle = "PEARC '20",

address = "United States",

note = "2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020 ; Conference date: 27-07-2020 Through 31-07-2020",

url = "https://pearc.acm.org/pearc20",

}

Febbo, R, Flood, B, Halloy, J, Lau, P, Wong, K & Ayala, A 2020, Autonomous Vehicle Control Using a Deep Neural Network and Jetson Nano. in PEARC '20: Practice and Experience in Advanced Research Computing. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 333-338, 2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020, Virtual, Online, United States, 27/07/20. https://doi.org/10.1145/3311790.3396669

Autonomous Vehicle Control Using a Deep Neural Network and Jetson Nano. / Febbo, Rocco; Flood, Brendan; Halloy, Julian et al.
PEARC '20: Practice and Experience in Advanced Research Computing. Association for Computing Machinery, 2020. p. 333-338 (ACM International Conference Proceeding Series).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Autonomous Vehicle Control Using a Deep Neural Network and Jetson Nano

AU - Febbo, Rocco

AU - Flood, Brendan

AU - Halloy, Julian

AU - Lau, Patrick

AU - Wong, Kwai

AU - Ayala, Alan

PY - 2020/7/26

Y1 - 2020/7/26

N2 - The idea of a self-driving car is one which is actively studied and tested for use on the road. In addition, the machine learning tools required to create such a vehicle has become more and more available to the public as time goes on. With a number of different libraries and softwares available for free download to design and train neural networks and with affordable but powerful miniature computers on the market, one can explore the possibility of creating a self-driving vehicle. The goal of our project was to construct such a car on a small scale using parts and software that are accessible to anyone on an affordable budget ($250), and to test the effectiveness of DNN software neural networks on training such a car. This project serves as a simple testbed for experimenting different ideas in self driving vehicle. Core ideas of autonomous vehicles are explored with machine learning in mind. The paper details the challenges and experience of project and is the result of an REU project support by the NSF.

AB - The idea of a self-driving car is one which is actively studied and tested for use on the road. In addition, the machine learning tools required to create such a vehicle has become more and more available to the public as time goes on. With a number of different libraries and softwares available for free download to design and train neural networks and with affordable but powerful miniature computers on the market, one can explore the possibility of creating a self-driving vehicle. The goal of our project was to construct such a car on a small scale using parts and software that are accessible to anyone on an affordable budget ($250), and to test the effectiveness of DNN software neural networks on training such a car. This project serves as a simple testbed for experimenting different ideas in self driving vehicle. Core ideas of autonomous vehicles are explored with machine learning in mind. The paper details the challenges and experience of project and is the result of an REU project support by the NSF.

KW - autonomous vehicle

KW - deep learning

KW - edge computing

KW - neural networks

KW - autonomous vehicle

KW - deep learning

KW - edge computing

KW - neural networks

KW - autonomous vehicle

KW - deep learning

KW - edge computing

KW - neural networks

UR - http://www.scopus.com/inward/record.url?scp=85089275099&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85089275099&origin=recordpage

U2 - 10.1145/3311790.3396669

DO - 10.1145/3311790.3396669

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-4503-6689-2

T3 - ACM International Conference Proceeding Series

SP - 333

EP - 338

BT - PEARC '20

PB - Association for Computing Machinery

T2 - 2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020

Y2 - 27 July 2020 through 31 July 2020

ER -

Autonomous Vehicle Control Using a Deep Neural Network and Jetson Nano

Abstract

Publication series

Conference

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this