Finding windows of opportunity: How drivers adapt to partial automation safeguards over time

IF 3.5 2区工程技术 Q1 PSYCHOLOGY, APPLIED Transportation Research Part F-Traffic Psychology and Behaviour Pub Date : 2025-02-28 DOI:10.1016/j.trf.2025.02.019

Alexandra S. Mueller , Pnina Gershon , Samantha H. Haus , Jessica B. Cicchino , Bruce Mehler , Bryan Reimer

{"title":"Finding windows of opportunity: How drivers adapt to partial automation safeguards over time","authors":"Alexandra S. Mueller , Pnina Gershon , Samantha H. Haus , Jessica B. Cicchino , Bruce Mehler , Bryan Reimer","doi":"10.1016/j.trf.2025.02.019","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><div>Most partially automated systems have safeguards to counteract driver disengagement, but little is known about how they affect driver behavior over time. This naturalistic observation study investigated how the behavior of 14 drivers who had no previous partial automation experience evolved over a month of exposure to the Tesla Autopilot system in a model year 2020 Model 3.</div></div><div><h3>Method</h3><div>Behavior was analyzed leading up to, during, and immediately after attention reminders and emergency-slowdown-leading-to-lockout events.</div></div><div><h3>Results</h3><div>We found that drivers learn to internalize safeguard sequences and discover windows of opportunity to do non-driving-related activities. People learned to respond quicker to alerts, leading to fewer escalated sequences in the latter half of the study. However, drivers also spent more time engaging in non-driving-related activities and glancing off-road, which corresponded with more initial alerts of the attention reminder sequence as time went on. Prolonged disengagement culminated in 16 lockouts across the sample, although in general, drivers responded faster and had fewer lockouts over time.</div></div><div><h3>Conclusion</h3><div>Our findings demonstrate the human ability to learn system constraints and thus illustrate that it is possible to shape safer driving behavior with robust safeguards. User-centric design considerations for driver support strategies are presented.</div></div>","PeriodicalId":48355,"journal":{"name":"Transportation Research Part F-Traffic Psychology and Behaviour","volume":"111 ","pages":"Pages 112-129"},"PeriodicalIF":3.5000,"publicationDate":"2025-02-28","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/S136984782500066X","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

Introduction

Most partially automated systems have safeguards to counteract driver disengagement, but little is known about how they affect driver behavior over time. This naturalistic observation study investigated how the behavior of 14 drivers who had no previous partial automation experience evolved over a month of exposure to the Tesla Autopilot system in a model year 2020 Model 3.

Method

Behavior was analyzed leading up to, during, and immediately after attention reminders and emergency-slowdown-leading-to-lockout events.

Results

We found that drivers learn to internalize safeguard sequences and discover windows of opportunity to do non-driving-related activities. People learned to respond quicker to alerts, leading to fewer escalated sequences in the latter half of the study. However, drivers also spent more time engaging in non-driving-related activities and glancing off-road, which corresponded with more initial alerts of the attention reminder sequence as time went on. Prolonged disengagement culminated in 16 lockouts across the sample, although in general, drivers responded faster and had fewer lockouts over time.

Conclusion

Our findings demonstrate the human ability to learn system constraints and thus illustrate that it is possible to shape safer driving behavior with robust safeguards. User-centric design considerations for driver support strategies are presented.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

求助全文

约1分钟内获得全文去求助

来源期刊

Transportation Research Part F-Traffic Psychology and Behaviour Multiple-

CiteScore

7.60

自引率

14.60%

发文量

239

审稿时长

71 days

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.