{"title":"注意解释:信息类型和错误类型影响自动驾驶车辆的信任和态势感知","authors":"Yaohan Ding;Lesong Jia;Na Du","doi":"10.1109/THMS.2025.3558437","DOIUrl":null,"url":null,"abstract":"Trust and situational awareness (SA) are critical for the acceptance and safety of automated vehicles (AVs). While AV explanations with different information types have been studied to enhance drivers' trust and SA, their effectiveness remains unclear when AVs make errors that do not trigger takeover requests. This study investigated the effects of information type, error type, and their interaction on drivers' trust in AVs, SA, and their relationships. We recruited 300 participants in an online video study with a 3 (information type: <italic>why</i>, <italic>how</i>, <italic>why + how</i>) × 3 (error type: false alarm, miss, correct [no error]) mixed design. <italic>How</i> information describes the vehicle's action, while <italic>why</i> information refers to the reason for the vehicle's action. Linear mixed models showed that false alarms and misses were associated with lower SA compared with correct scenarios, but possibly due to different reasons. Compared with correct scenarios, both false alarms and misses were associated with lower trust, with misses even lower than false alarms, possibly due to the varying severity of potential consequences. Compared with <italic>why</i> and <italic>why + how</i> information, <italic>how</i> information was generally associated with lower SA and a higher potential of overtrust in false alarms. Trust and SA had a negative linear relationship in misses and false alarms, while no correlations were found in correct scenarios. To mitigate potential overtrust and misinterpretation of situations when AVs make errors, it is crucial to maintain higher SA. We recommend including <italic>why</i> information in AV explanations and deploying AV decision systems that are less miss-prone.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 3","pages":"450-459"},"PeriodicalIF":4.4000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Watch Out for Explanations: Information Type and Error Type Affect Trust and Situational Awareness in Automated Vehicles\",\"authors\":\"Yaohan Ding;Lesong Jia;Na Du\",\"doi\":\"10.1109/THMS.2025.3558437\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Trust and situational awareness (SA) are critical for the acceptance and safety of automated vehicles (AVs). While AV explanations with different information types have been studied to enhance drivers' trust and SA, their effectiveness remains unclear when AVs make errors that do not trigger takeover requests. This study investigated the effects of information type, error type, and their interaction on drivers' trust in AVs, SA, and their relationships. We recruited 300 participants in an online video study with a 3 (information type: <italic>why</i>, <italic>how</i>, <italic>why + how</i>) × 3 (error type: false alarm, miss, correct [no error]) mixed design. <italic>How</i> information describes the vehicle's action, while <italic>why</i> information refers to the reason for the vehicle's action. Linear mixed models showed that false alarms and misses were associated with lower SA compared with correct scenarios, but possibly due to different reasons. Compared with correct scenarios, both false alarms and misses were associated with lower trust, with misses even lower than false alarms, possibly due to the varying severity of potential consequences. Compared with <italic>why</i> and <italic>why + how</i> information, <italic>how</i> information was generally associated with lower SA and a higher potential of overtrust in false alarms. Trust and SA had a negative linear relationship in misses and false alarms, while no correlations were found in correct scenarios. To mitigate potential overtrust and misinterpretation of situations when AVs make errors, it is crucial to maintain higher SA. We recommend including <italic>why</i> information in AV explanations and deploying AV decision systems that are less miss-prone.\",\"PeriodicalId\":48916,\"journal\":{\"name\":\"IEEE Transactions on Human-Machine Systems\",\"volume\":\"55 3\",\"pages\":\"450-459\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Human-Machine Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10980640/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Human-Machine Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10980640/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Watch Out for Explanations: Information Type and Error Type Affect Trust and Situational Awareness in Automated Vehicles
Trust and situational awareness (SA) are critical for the acceptance and safety of automated vehicles (AVs). While AV explanations with different information types have been studied to enhance drivers' trust and SA, their effectiveness remains unclear when AVs make errors that do not trigger takeover requests. This study investigated the effects of information type, error type, and their interaction on drivers' trust in AVs, SA, and their relationships. We recruited 300 participants in an online video study with a 3 (information type: why, how, why + how) × 3 (error type: false alarm, miss, correct [no error]) mixed design. How information describes the vehicle's action, while why information refers to the reason for the vehicle's action. Linear mixed models showed that false alarms and misses were associated with lower SA compared with correct scenarios, but possibly due to different reasons. Compared with correct scenarios, both false alarms and misses were associated with lower trust, with misses even lower than false alarms, possibly due to the varying severity of potential consequences. Compared with why and why + how information, how information was generally associated with lower SA and a higher potential of overtrust in false alarms. Trust and SA had a negative linear relationship in misses and false alarms, while no correlations were found in correct scenarios. To mitigate potential overtrust and misinterpretation of situations when AVs make errors, it is crucial to maintain higher SA. We recommend including why information in AV explanations and deploying AV decision systems that are less miss-prone.
期刊介绍:
The scope of the IEEE Transactions on Human-Machine Systems includes the fields of human machine systems. It covers human systems and human organizational interactions including cognitive ergonomics, system test and evaluation, and human information processing concerns in systems and organizations.