{"title":"Impact of level 2 automation on driver behavior: A study using association rules mining","authors":"Rohit Chakraborty , Syed Aaqib Javed , Subasish Das , Boniphace Kutela , Md Nasim Khan","doi":"10.1016/j.trf.2024.10.016","DOIUrl":null,"url":null,"abstract":"<div><div>Driver distraction and reduced situational awareness pose significant risks in vehicles with Level 2 (L2) automation systems, such as adaptive cruise control and lane-keeping assistance. This study analyzed naturalistic driving data using Association Rules Mining (ARM) to investigate the impact of L2 automation on driver behavior. The dataset included 771 driving events categorized by L2 system activation status (active or inactive), intersection types, and hand positions on the steering wheel. Key variables were analyzed, such as eyes-off-road (EOR) time, off-road glance frequency and duration, and the influence of different driving conditions. The findings revealed that driver distraction, indicated by longer EOR times and more frequent off-road glances, is significantly higher when L2 systems are active. Additionally, drivers exhibit the highest levels of inattention with no hands on the wheel during L2 activation. These insights highlighted the need for improved driver-system interfaces. They targeted driver education to enhance the safety and effectiveness of L2 automation, ultimately contributing to safer roadways and better-informed policy decisions.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"107 ","pages":"Pages 937-950"},"PeriodicalIF":3.5000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part F-Traffic Psychology and Behaviour","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1369847824002936","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
Driver distraction and reduced situational awareness pose significant risks in vehicles with Level 2 (L2) automation systems, such as adaptive cruise control and lane-keeping assistance. This study analyzed naturalistic driving data using Association Rules Mining (ARM) to investigate the impact of L2 automation on driver behavior. The dataset included 771 driving events categorized by L2 system activation status (active or inactive), intersection types, and hand positions on the steering wheel. Key variables were analyzed, such as eyes-off-road (EOR) time, off-road glance frequency and duration, and the influence of different driving conditions. The findings revealed that driver distraction, indicated by longer EOR times and more frequent off-road glances, is significantly higher when L2 systems are active. Additionally, drivers exhibit the highest levels of inattention with no hands on the wheel during L2 activation. These insights highlighted the need for improved driver-system interfaces. They targeted driver education to enhance the safety and effectiveness of L2 automation, ultimately contributing to safer roadways and better-informed policy decisions.
期刊介绍:
Transportation Research Part F: Traffic Psychology and Behaviour focuses on the behavioural and psychological aspects of traffic and transport. The aim of the journal is to enhance theory development, improve the quality of empirical studies and to stimulate the application of research findings in practice. TRF provides a focus and a means of communication for the considerable amount of research activities that are now being carried out in this field. The journal provides a forum for transportation researchers, psychologists, ergonomists, engineers and policy-makers with an interest in traffic and transport psychology.