Mark Colley, Oliver Speidel, Jan Strohbeck, J. Rixen, Janina Belz, Enrico Rukzio
{"title":"高度自动驾驶汽车中的不确定轨迹预测可视化对信任、情景意识和认知负荷的影响","authors":"Mark Colley, Oliver Speidel, Jan Strohbeck, J. Rixen, Janina Belz, Enrico Rukzio","doi":"10.1145/3631408","DOIUrl":null,"url":null,"abstract":"Automated vehicles are expected to improve safety, mobility, and inclusion. User acceptance is required for the successful introduction of this technology. One essential prerequisite for acceptance is appropriately trusting the vehicle's capabilities. System transparency via visualizing internal information could calibrate this trust by enabling the surveillance of the vehicle's detection and prediction capabilities, including its failures. Additionally, concurrently increased situation awareness could improve take-overs in case of emergency. This work reports the results of two online comparative video-based studies on visualizing prediction and maneuver-planning information. Effects on trust, cognitive load, and situation awareness were measured using a simulation (N=280) and state-of-the-art road user prediction and maneuver planning on a pre-recorded real-world video using a real prototype (N=238). Results show that color conveys uncertainty best, that the planned trajectory increased trust, and that the visualization of other predicted trajectories improved perceived safety.","PeriodicalId":20553,"journal":{"name":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","volume":"3 12","pages":"1 - 23"},"PeriodicalIF":3.6000,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effects of Uncertain Trajectory Prediction Visualization in Highly Automated Vehicles on Trust, Situation Awareness, and Cognitive Load\",\"authors\":\"Mark Colley, Oliver Speidel, Jan Strohbeck, J. Rixen, Janina Belz, Enrico Rukzio\",\"doi\":\"10.1145/3631408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automated vehicles are expected to improve safety, mobility, and inclusion. User acceptance is required for the successful introduction of this technology. One essential prerequisite for acceptance is appropriately trusting the vehicle's capabilities. System transparency via visualizing internal information could calibrate this trust by enabling the surveillance of the vehicle's detection and prediction capabilities, including its failures. Additionally, concurrently increased situation awareness could improve take-overs in case of emergency. This work reports the results of two online comparative video-based studies on visualizing prediction and maneuver-planning information. Effects on trust, cognitive load, and situation awareness were measured using a simulation (N=280) and state-of-the-art road user prediction and maneuver planning on a pre-recorded real-world video using a real prototype (N=238). Results show that color conveys uncertainty best, that the planned trajectory increased trust, and that the visualization of other predicted trajectories improved perceived safety.\",\"PeriodicalId\":20553,\"journal\":{\"name\":\"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies\",\"volume\":\"3 12\",\"pages\":\"1 - 23\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-01-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3631408\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3631408","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Effects of Uncertain Trajectory Prediction Visualization in Highly Automated Vehicles on Trust, Situation Awareness, and Cognitive Load
Automated vehicles are expected to improve safety, mobility, and inclusion. User acceptance is required for the successful introduction of this technology. One essential prerequisite for acceptance is appropriately trusting the vehicle's capabilities. System transparency via visualizing internal information could calibrate this trust by enabling the surveillance of the vehicle's detection and prediction capabilities, including its failures. Additionally, concurrently increased situation awareness could improve take-overs in case of emergency. This work reports the results of two online comparative video-based studies on visualizing prediction and maneuver-planning information. Effects on trust, cognitive load, and situation awareness were measured using a simulation (N=280) and state-of-the-art road user prediction and maneuver planning on a pre-recorded real-world video using a real prototype (N=238). Results show that color conveys uncertainty best, that the planned trajectory increased trust, and that the visualization of other predicted trajectories improved perceived safety.