English(EN) Context-Aware Causal Gaze Forecasting for Human-Vehicle Interaction During In-Cabin Tracking Dropouts

新AI预测追踪中断期间的驾驶员注视点

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员开发了一种名为因果上下文门控预测器（Causal Context-Gated Forecaster, CCGF）的新方法，用于预测车内追踪中断期间的驾驶员注视点。该系统旨在预测驾驶员的视觉注意力，即使在标准的注视追踪器失去其眼睛视野时也能做到，这种情况在转头检查等头部运动中很常见。CCGF利用注视和头部姿态数据的历史记录，并结合来自DINOv3的场景特征来进行预测。在中断期间使用实时场景更新时，该系统比以前的方法将误差降低了33%。 AI

影响这项研究可以通过在关键驾驶操作期间提供更可靠的注视追踪来改进驾驶员监控系统。

排序理由学术论文，详细介绍了一种用于特定应用的新预测模型。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Shabnam Shabani, Ghazal Farhani · 2026-09-14 04:00

面向车内追踪中断时人车交互的上下文感知因果注视预测

arXiv:2609.12374v1 Announce Type: new Abstract: Dashboard-mounted gaze trackers often lose sight of the driver's eyes during large head rotations, including shoulder checks, mirror glances, and intersection scanning. These maneuvers occur when information about the driver's visua…

本文如何被排名

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面向车内追踪中断时人车交互的上下文感知因果注视预测

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