Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network

Jamal Raiyn; Galia Weidl

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network

Topics: Autonomous Vehicles and Automated Driving; Driver Behavior Analysis; Traffic and Vehicle Data Collection and Processing

In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems VEHITS - Volume 1, 248-256, 2023 , Prague, Czech Republic

Authors: Jamal Raiyn ¹ and Galia Weidl ²

Affiliations: ¹ Computer Science Department, Al Qasemi Academic College, Baqa Al Ghariah, Israel ; ² Connected Urban Mobility, Faculty of Engineering, Technical University of Applied Sciences, Aschaffenburg, Germany

Keyword(s): Convolutional Neural Network, Deep Learning, Autonomous Vehicle, Anomalous Data.

Abstract: The new generation of autonomous vehicles (AVs) are being designed to act autonomously and collect travel data based on various smart devices and sensors. The goal is to enable AVs to operate under their own power. Naturalistic driving studies (NDSs) collect data continuously from real traffic activities, in order not to miss any safety-critical event. In NDSs of AVs, however, the data they collect is influenced by various sources that degrade their forecasting accuracy. A convolutional neural network (CNN) is proposed to process a large amount of traffic data in different formats. A CNN can detect anomalies in traffic data that negatively affect traffic efficiency and identify the source of data anomalies, which can help reduce traffic congestion and vehicular queuing.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 18.221.89.241

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

Raiyn, J. and Weidl, G. (2023). Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network. In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-652-1; ISSN 2184-495X, SciTePress, pages 248-256. DOI: 10.5220/0011839600003479

@conference{vehits23,
author={Jamal Raiyn and Galia Weidl},
title={Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network},
booktitle={Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2023},
pages={248-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011839600003479},
isbn={978-989-758-652-1},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network
SN - 978-989-758-652-1
IS - 2184-495X
AU - Raiyn, J.
AU - Weidl, G.
PY - 2023
SP - 248
EP - 256
DO - 10.5220/0011839600003479
PB - SciTePress