AI 中文总结
研究探讨可控视觉模糊对驾驶的影响，提出BlurDriving系统及人在回路多目标贝叶斯优化框架实现个性化。通过两项VR用户研究发现，个性化虽有个体模糊偏好差异，但未显著提升客观驾驶性能，其效果因个体而异，为车载界面设计提供参考。
AI 中文摘要
分心驾驶仍是主要安全隐患，促使人们寻求减少视觉过载的方法。但视觉过载因人而异，难以确定适合每位驾驶员的干预措施。我们研究可控视觉模糊能否简化驾驶场景并减轻分心。为此提出BlurDriving，一种虚拟现实城市驾驶模拟器中的目标选择性、距离感知模糊系统，采用人在回路多目标贝叶斯优化框架实现模糊配置个性化。在两项虚拟现实用户研究中，分别在正常和认知要求高的条件下评估驾驶情况。发现个性化显示出模糊偏好的强烈个体差异，但与无模糊基线相比，客观驾驶性能无显著改善。定性反馈显示两极化反应。这些发现表明视觉模糊并非普遍有效，其益处取决于个体感知策略和对视觉不确定性的容忍度。这项工作凸显了在安全关键驾驶中个性化视觉简化的局限性，并为自适应车载界面设计提供了参考。
英文摘要
Distracted driving remains a major safety concern, motivating approaches that aim to reduce visual overload before attention breaks down. However, visual overload varies across individuals, making it difficult to determine appropriate interventions for each driver. We investigate whether controllable visual blur can simplify the driving scene and mitigate distraction. To address this challenge, we propose BlurDriving, a target-selective, distance-aware blur system in a Virtual Reality (VR) urban driving simulator, and employ a Human-in-the-Loop Multi-Objective Bayesian Optimization (HITL-MOBO) framework to personalize blur configurations. Across two VR user studies, we evaluated driving under normal conditions in Study 1 and under cognitively demanding conditions in Study 2. We found that personalization revealed strong individual differences in blur preference but did not lead to significant improvements in objective driving performance compared to a no-blur baseline. Qualitative feedback revealed polarized responses: some drivers reported improved focus, while others experienced uncertainty, fatigue, or discomfort. These findings suggest that visual blur is not universally effective. Instead, its benefits depend on individual perceptual strategies and tolerance for visual uncertainty. This work highlights the limits of personalized visual simplification in safety-critical driving and informs adaptive in-vehicle interface design.
DOI：10.1145/3831646
