Human drivers must quickly adjust to perturbations at tunnel entrances (i.e., the rapid switching of cross-sections, abrupt longitudinal changes in the driving environment, and changes in visual illumination, denoted “tunnel transition perturbations”) to regain control of their vehicles, especially when managing speed to prevent motor overshoot. Previous research has assessed drivers’ visual adaptation rather than variations in vehicle control under tunnel transition perturbations. In this study, a sample entropy method was used to measure the safety–critical duration of speed control events at tunnel entrances and thereby reveal the participants’ speed adaptation and recovery performance under tunnel transition perturbations. Two key metrics—model invalidation time and performance recovery time—were introduced, and an uncorrelated grouped random parameters hazard-based duration model was developed. Road grade, road curvature, income, and time having held a license were positively associated with model invalidation time, while a history of accidents in the past 12 months was negatively associated with model invalidation time. In addition, road grade, road curvature, and income had heterogeneous effects on model invalidation time. Moreover, a history of accidents in the past 12 months moderated the relationship between road grade and model invalidation time. Furthermore, road curvature, average annual mileage, and sleep deprivation significantly influenced performance recovery time, while road grade and non-fatigue condition had heterogeneous effects on performance recovery time. Overall, this study demonstrated that the participants’ personal characteristics and experiences significantly shaped the development of their internal models, and that their current status and perception had a substantial influence on their performance recovery under tunnel transition perturbations. These insights enhance understanding of the mechanisms of drivers’ motor control under tunnel transition perturbations and will therefore enable improvement of road traffic design and safety management at tunnel entrances.

中文翻译：

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司机如何在隧道入口处管理速度？来自不相关的分组随机参数持续时间模型的见解，用于模型失效和性能恢复时间


人类驾驶员必须迅速适应隧道入口处的扰动（即横截面的快速切换、驾驶环境的突然纵向变化以及视觉照明的变化，称为“隧道过渡扰动”）以重新获得对车辆的控制，尤其是在管理速度以防止电机超调时。以前的研究评估了驾驶员的视觉适应能力，而不是隧道过渡扰动下车辆控制的变化。本研究采用样本熵法测量隧道入口处速度控制事件的安全临界持续时间，从而揭示参与者在隧道过渡扰动下的速度适应和恢复性能。引入了两个关键指标——模型失效时间和性能恢复时间——并开发了一个不相关的分组随机参数基于危害的持续时间模型。道路等级、道路曲率、收入和持有驾照的时间与模型失效时间呈正相关，而过去 12 个月的事故史与模型失效时间呈负相关。此外，道路等级、道路曲率和收入对模型失效时间有异质性影响。此外，过去 12 个月的事故历史调节了道路等级与模型失效时间之间的关系。此外，道路曲率、平均年行驶里程和睡眠剥夺对性能恢复时间有显著影响，而道路等级和非疲劳条件对性能恢复时间有异质性影响。 总体而言，这项研究表明，参与者的个人特征和经历显着影响了他们内部模型的发展，他们的当前状态和感知对他们在隧道过渡扰动下的表现恢复产生了重大影响。这些见解增强了对隧道过渡扰动下驾驶员电机控制机制的理解，因此将有助于改进隧道入口的道路交通设计和安全管理。