发表机构
Hochschule München（慕尼黑应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出首个统一框架，在真实驾驶条件下量化可穿戴眼动追踪精度，通过室内外实验将平均误差从4.58度降至1.10度，并指出可靠BEV监督需在线校准和条件相关的不确定性估计。
AI 中文摘要
鸟瞰图（BEV）表示已成为自动驾驶中感知与规划之间广泛使用的接口，但它们编码的是场景中存在什么，而非对人类驾驶员具有行为相关性的信息。注视（Gaze）为这一差距提供了一种有力的行为信号，然而可穿戴眼动追踪器在实际部署中常被当作其空间输出即为真值，尽管它们对头部运动、光照和校准漂移具有已知的敏感性。我们提出了据我们所知首个在真实驾驶条件下量化可穿戴眼动追踪精度的统一框架。我们的道路实验包含41个经过验证的场景，其中一名驾驶员注视车辆的牌照。注视误差被测量为牌照中心与眼镜估计的注视方向之间的角度差异。使用同一驾驶员和设备的独立室内实验系统分析了距离、光照、头部运动、目标运动和注视偏心率如何影响系统偏差和注视精度。平均道路误差为4.58度。应用从室内记录中估计的偏移量将其降低至1.10度，并改善了所有41个场景。由于该偏移量在不同会话之间有所变化，可靠的BEV监督可能需要在线重新校准和基于条件的注视不确定性估计。
英文摘要
Bird's-eye-view (BEV) representations have become a widely used interface between perception and planning in autonomous driving, but they encode what is in a scene, not what is behaviorally relevant to a human driver. Gaze offers a compelling behavioral signal for this gap, yet wearable eye trackers are routinely deployed as if their spatial output were ground truth, despite known sensitivity to head motion, illumination, and calibration drift. We present, to our knowledge, the first unified framework for quantifying wearable gaze accuracy under real driving conditions. Our on-road study contains 41 validated scenes in which one driver fixated a vehicle's license plate. Gaze error is measured as the angular difference between the plate center and the gaze direction estimated by the glasses. Separate indoor studies with the same driver and device systematically analyze how distance, illumination, head motion, target motion, and gaze eccentricity affect both systematic bias and gaze precision. The mean on-road error was 4.58 degrees. Applying an offset estimated from the indoor recordings reduced it to 1.10 degrees and improved all 41 scenes. Because this offset varied between sessions, reliable BEV supervision may require online recalibration and condition-dependent estimates of gaze uncertainty.
CommentsPeer-reviewed and accepted as an Extended Abstract at the German Conference on Pattern Recognition (GCPR 2026). Presented as a poster at GCPR 2026
