This study aims to explore the dynamics of driver attention to various zones, including the road, the central mirror, the embedded Human-Machine Interface (HMI), and the speedometer, across different driving modes in AVs. The integration of autonomous vehicles (AVs) into transportation systems has introduced critical safety concerns, particularly regarding driver re-engagement during mode transitions. Past accidents underscore the risks of overreliance on automation and highlight the need to understand dynamic attention allocation to support safety in autonomous driving. A high-fidelity driving simulation was conducted. Eye-tracking technology was used to measure fixation duration, fixation count, and time to first fixation across distinct driving modes (automated, manual, and transition), which were then used to assess how drivers allocated attention to various areas of interest (AOIs). Findings show that drivers' attention varies significantly across driving modes. In manual mode, attention consistently focuses on the road, while in automated mode, prolonged fixation on the embedded HMI was observed. During the handover and takeover phases, attention shifts dynamically between environmental and technological elements. The study reveals that driver attention allocation is mode-dependent. These findings inform the design of adaptive HMIs in AVs that align with drivers' attention patterns. By presenting relevant information according to the driving context, such systems can enhance driver-vehicle interaction, support effective transitions, and improve overall safety. Systematic analysis of visual attention dynamics across driving modes is gaining prominence, as it informs adaptive HMI designs and driver readiness interventions. The GLMM findings can be directly applied to the design of adaptive HMIs or driver training programs to enhance attention and improve safety.

翻译：本研究旨在探索自动驾驶汽车（AVs）中，驾驶员在不同驾驶模式下对道路、中央后视镜、嵌入式人机界面（HMI）及速度表等多个区域的注意力动态特征。自动驾驶汽车（AVs）融入交通系统引发了关键的安全问题，尤其是在模式转换过程中驾驶员重新接管车辆的风险。以往的事故凸显了过度依赖自动化的危险性，并强调了理解动态注意力分配对于保障自动驾驶安全的重要性。本研究进行了一项高保真驾驶模拟实验，利用眼动追踪技术测量了不同驾驶模式（自动、手动及过渡阶段）下的注视时长、注视次数及首次注视时间，进而评估驾驶员如何将注意力分配到不同的兴趣区域（AOIs）。研究发现，驾驶员的注意力在不同驾驶模式下存在显著差异。在手动模式下，注意力持续集中于道路；而在自动模式下，则观察到对嵌入式HMI的长时间注视。在交接与接管阶段，注意力在环境要素与技术要素之间动态切换。研究表明，驾驶员的注意力分配具有模式依赖性。这些发现为设计符合驾驶员注意力模式的自适应HMI提供了依据。通过根据驾驶情境呈现相关信息，此类系统能够增强人车交互，支持有效的模式转换，并提升整体安全性。对不同驾驶模式下视觉注意力动态的系统分析正日益受到重视，因为它能为自适应HMI设计及驾驶员准备状态干预措施提供指导。广义线性混合模型（GLMM）的研究结果可直接应用于自适应HMI设计或驾驶员培训项目，以优化注意力分配并提升安全性。