AI 中文总结
该研究针对自动驾驶汽车可视化设计优化的痛点，采用多目标贝叶斯优化方法，通过多会话迭代优化，在线实验验证其可提升乘客信任度等，同时也指出计算优化主观测量的不足。
AI 中文摘要
理解自动驾驶汽车（AVs）对提高其接受度至关重要，已有多种向乘客可视化相关交通信息的方法被提出并实证评估，但该过程耗时、成本高且会减少可能的设计参数，因此我们采用多目标贝叶斯优化来优化AVs中的可视化设计，重点评估涉及迭代优化的多会话方面，我们以乘客信任度和感知安全性为优化目标，同时最小化认知负荷，在线研究（N=74）的结果表明，该方法能有效识别可提高信任度、安全性和可预测性的可视化设计参数值，同时使设计过程更高效、可扩展，不过研究也指出并讨论了计算方法在优化主观测量时存在的不足。
英文摘要
Understanding automated vehicles (AVs) is crucial to improving their acceptance. Numerous approaches to visualizing relevant traffic information to passengers have been proposed and empirically evaluated. As this is time-consuming, costly, and reduces the possible design parameters, we employed multi-objective Bayesian optimization to optimize the design of visualizations in AVs. In particular, we evaluated multi-session aspects involving iterative optimization. We optimized the design for passenger trust and perceived safety while minimizing cognitive load. Results from an online study (N=74) show that this method effectively identifies visualization design parameter values that improve trust, safety, and predictability while making the design process more efficient and scalable. However, shortcomings of the computational approach when optimizing for subjective measurements are highlighted and discussed.
Commentsaccepted to AutomotiveUI 2026
