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.

翻译：自动驾驶车辆（AV）的接受度依赖于通过反馈机制增进用户理解。虽然可视化技术旨在提升用户对AV感知、预测与规划功能的理解，但确立最优设计方案仍具挑战性。传统"一刀切"设计可能因资源密集的实证评估而存在局限。本文提出OptiCarVis——一套采用多目标贝叶斯优化（MOBO）的人类在环（HITL）方法，用于优化AV反馈可视化。我们通过八种专家设计与用户定制设计，对预热启动HITL MOBO方案进行对比验证。在线研究（N=117）表明，OptiCarVis能显著提升信任度、接受度、感知安全性与可预测性，且未增加认知负荷。OptiCarVis支持全面的设计空间探索，可优化车载界面以提升乘客体验，并具备更广泛的适用性。