OptiCarVis：利用贝叶斯优化改进自动驾驶车辆功能可视化以提升用户体验
OptiCarVis: Improving Automated Vehicle Functionality Visualizations Using Bayesian Optimization to Enhance User Experience
AI总结：
本文提出OptiCarVis，一种基于人在回路多目标贝叶斯优化的自动驾驶可视化优化方法，通过在线实验证明其能在不增加认知负荷的情况下显著提升用户信任、接受度、感知安全性与可预测性。
摘要：
自动驾驶车辆（AV）的接受度依赖于用户通过反馈对其的理解。虽然可视化旨在增强用户对AV的检测、预测和规划功能的理解，但建立最优设计具有挑战性。传统的“一刀切”设计可能并不适用，这源于资源密集型的实证评估。本文介绍了OptiCarVis，一套人在回路（HITL）方法，使用多目标贝叶斯优化（MOBO）来优化AV反馈可视化。我们使用八个专家和用户定制设计对热启动HITL MOBO的条件进行了比较。一项在线研究（N=117）表明，OptiCarVis在显著提高信任、接受度、感知安全性和可预测性的同时，并未增加认知负荷。OptiCarVis促进了全面的设计空间探索，增强了车载界面，以实现最佳乘客体验和更广泛的适用性。
英文摘要：
Automated vehicle (AV) acceptance relies on their understanding via feedback. While visualizations aim to enhance user understanding of AV's detection, prediction, and planning functionalities, establishing an optimal design is challenging. Traditional "one-size-fits-all" designs might be unsuitable, stemming from resource-intensive empirical evaluations. This paper introduces OptiCarVis, a set of Human-in-the-Loop (HITL) approaches using Multi-Objective Bayesian Optimization (MOBO) to optimize AV feedback visualizations. We compare conditions using eight expert and user-customized designs for a Warm-Start HITL MOBO. An online study (N=117) demonstrates OptiCarVis's efficacy in significantly improving trust, acceptance, perceived safety, and predictability without increasing cognitive load. OptiCarVis facilitates a comprehensive design space exploration, enhancing in-vehicle interfaces for optimal passenger experiences and broader applicability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
