{"title":"Repeated Route Naturalistic Driver Behavior Analysis Using Motion and Gaze Measurements","authors":"Bikram Adhikari;Zoran Durić;Duminda Wijesekera;Bo Yu","doi":"10.1109/TITS.2024.3520893","DOIUrl":null,"url":null,"abstract":"Due to advancements in intelligent transportation and the emergence of automated vehicles, interest in analyzing driver behavior to improve commuters’ driving experiences has surged. Past studies have utilized driver gaze data to analyze behavior under various driving conditions using machine learning techniques. However, exploring driver behavior through multiple modalities can provide deeper insights. To this end, we conducted a naturalistic driver behavior study with ten participants, collecting vehicular data and driver gaze measurements using standard sensors. This dataset allows for an accurate assessment of driver behavior across different road types, traffic conditions, and congestion levels. Additionally, we investigated the influence of driving experience and time of day on behavior. Experienced drivers showed greater consistency across scenarios, while novices’ performance varied based on traffic intensity and route type.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3273-3283"},"PeriodicalIF":8.4000,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Intelligent Transportation Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10818997/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
Due to advancements in intelligent transportation and the emergence of automated vehicles, interest in analyzing driver behavior to improve commuters’ driving experiences has surged. Past studies have utilized driver gaze data to analyze behavior under various driving conditions using machine learning techniques. However, exploring driver behavior through multiple modalities can provide deeper insights. To this end, we conducted a naturalistic driver behavior study with ten participants, collecting vehicular data and driver gaze measurements using standard sensors. This dataset allows for an accurate assessment of driver behavior across different road types, traffic conditions, and congestion levels. Additionally, we investigated the influence of driving experience and time of day on behavior. Experienced drivers showed greater consistency across scenarios, while novices’ performance varied based on traffic intensity and route type.
期刊介绍：
The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.
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