Marios Sekadakis , Sandra Trösterer , Peter Moertl , George Yannis
{"title":"Identifying driving profiles after take over request in automated vehicles at SAE levels 2 and 3","authors":"Marios Sekadakis , Sandra Trösterer , Peter Moertl , George Yannis","doi":"10.1016/j.trf.2025.03.007","DOIUrl":null,"url":null,"abstract":"<div><div>Automated Driving (AD) has the potential to significantly reshape the transportation industry by improving safety, efficiency, and user comfort and acceptance. This research investigates driver behaviors during Take Over Requests (TORs) in automated vehicles at SAE Levels 2 and 3 using the HADRIAN Human-Machine Interface (HMI), designed to enhance driver support through real-time feedback and countdown displays. Analysis included clustering to develop distinct driving profiles based on key measurements collected through a driving simulator experiment, such as acceleration, deceleration, and speed, offering a deep understanding of driver behavior in responses to TORs. Three primary driving profiles were identified: “Passive driving and slow TOR response”, “Nervous driving and moderate TOR response”, and “Normal driving and quick TOR response”. This study also provides specific insights for each automation level, identifying profiles of driver behaviors at both SAE Levels 2 and 3. Results reveal that the nervous driving profile, although less frequent, poses significant safety implications due to higher deceleration rates and variability in speed and deceleration. Additionally, the study highlights that Non-Driving Related Tasks (NDRTs) increase the need for longer Take Over Time (TOT), with greater variability observed at higher automation levels, making accurate estimation of required TOT more challenging. The HADRIAN HMI is shown to positively impact driver performance by increasing TOT, allowing drivers more time to transition from automated to manual control comfortably. These insights can inform the design of more adaptive HMI systems, enhance real-time feedback mechanisms, and improve driver training programs to ensure safer transitions during TORs.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"111 ","pages":"Pages 250-263"},"PeriodicalIF":3.5000,"publicationDate":"2025-03-12","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/S1369847825000993","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
Automated Driving (AD) has the potential to significantly reshape the transportation industry by improving safety, efficiency, and user comfort and acceptance. This research investigates driver behaviors during Take Over Requests (TORs) in automated vehicles at SAE Levels 2 and 3 using the HADRIAN Human-Machine Interface (HMI), designed to enhance driver support through real-time feedback and countdown displays. Analysis included clustering to develop distinct driving profiles based on key measurements collected through a driving simulator experiment, such as acceleration, deceleration, and speed, offering a deep understanding of driver behavior in responses to TORs. Three primary driving profiles were identified: “Passive driving and slow TOR response”, “Nervous driving and moderate TOR response”, and “Normal driving and quick TOR response”. This study also provides specific insights for each automation level, identifying profiles of driver behaviors at both SAE Levels 2 and 3. Results reveal that the nervous driving profile, although less frequent, poses significant safety implications due to higher deceleration rates and variability in speed and deceleration. Additionally, the study highlights that Non-Driving Related Tasks (NDRTs) increase the need for longer Take Over Time (TOT), with greater variability observed at higher automation levels, making accurate estimation of required TOT more challenging. The HADRIAN HMI is shown to positively impact driver performance by increasing TOT, allowing drivers more time to transition from automated to manual control comfortably. These insights can inform the design of more adaptive HMI systems, enhance real-time feedback mechanisms, and improve driver training programs to ensure safer transitions during TORs.
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.