Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network

Jamal Raiyn, Galia Weidl

2023

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.

Paper Citation

in Harvard Style

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 - Volume 1: VEHITS, ISBN 978-989-758-652-1, SciTePress, pages 248-256. DOI: 10.5220/0011839600003479

in Bibtex Style

@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 - Volume 1: VEHITS,},
year={2023},
pages={248-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011839600003479},
isbn={978-989-758-652-1},
}

in EndNote Style

TY - CONF

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