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Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network

  • 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.

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Metadaten
Author:Jamal RaiynORCiD, Galia WeidlORCiD
URL:https://www.researchgate.net/publication/368332673_Naturalistic_Driving_Studies_Data_Analysis_Based_on_a_Convolutional_Neural_Network#fullTextFileContent
DOI:https://doi.org/10.5220/0011839600003479
Parent Title (English):VEHITS 2023: 9th International Conference on Vehicle Technology and Intelligent Transport Systems
Document Type:Conference Proceeding
Language:English
Year of Completion:2023
Release Date:2023/12/06
GND Keyword:Autonomes Fahrzeug; Künstliche Intelligenz
Urheberrecht:1
Institutes:Einrichtungen / Kompetenzzentrum Künstliche Intelligenz
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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