Effects of Uncertain Trajectory Prediction Visualization in Highly Automated Vehicles on Trust, Situation Awareness, and Cognitive Load

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Pub Date : 2024-01-12 DOI:10.1145/3631408
Mark Colley, Oliver Speidel, Jan Strohbeck, J. Rixen, Janina Belz, Enrico Rukzio
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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.
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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
自动驾驶汽车有望提高安全性、机动性和包容性。要成功引入这项技术,用户必须接受。接受的一个基本前提是适当信任车辆的能力。通过可视化内部信息实现系统透明化,可以监控车辆的检测和预测能力,包括其故障,从而校准这种信任。此外,同时增强的态势感知能力可以改善紧急情况下的接管能力。这项工作报告了两项基于视频的在线比较研究结果,研究内容是预测和机动规划信息的可视化。研究使用模拟(280 人)和使用真实原型(238 人)在预先录制的真实世界视频上测量了对信任、认知负荷和态势感知的影响。结果表明,颜色最能体现不确定性,规划的轨迹增加了信任感,其他预测轨迹的可视化提高了感知安全性。
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来源期刊
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
CiteScore
9.10
自引率
0.00%
发文量
154
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