Adverse weather has a considerable impact on the behavior of drivers, which puts vehicles and drivers in hazardous situations that can easily cause traffic accidents. This research examines how drivers’ perceived risk changes during car following under different adverse weather conditions by using driving simulation experiment. An expressway road scenario was built in a driving simulator. Eleven types of weather conditions, including clear sky, four levels of fog, four levels of rain and two levels of snow, were designed. Furthermore, to simulate the car-following behavior, three car-following situations were designed according to the motion of the lead car. Seven car-following indicators were extracted based on risk homeostasis theory. Then, the entropy weight method was used to integrate the selected indicators into an index to represent the drivers’ perceived risk. Multiple linear regression was applied to measure the influence of adverse weather conditions on perceived risk, and the coefficients were considered as indicators. The results demonstrate that both the weather conditions and road type have significant effects on car-following behavior. Drivers’ perceived risk tends to increase with the worsening weather conditions. Under conditions of extremely poor visibility, such as heavy dense fog, the measured drivers’ perceived risk is low due to the difficulties in vehicle operation and limited visibility.

中文翻译：

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基于驾驶模拟，恶劣天气对驾驶员在跟车过程中感知风险的影响

恶劣天气会对驾驶员的行为产生重大影响，从而使车辆和驾驶员处于危险状态，容易造成交通事故。这项研究通过使用驾驶模拟实验研究了驾驶员在不同不利天气条件下跟随汽车时感知到的风险变化。在驾驶模拟器中构建了高速公路道路场景。设计了11种天气条件，包括晴朗的天空，四个雾水平，四个雨水平和两个雪水平。此外，为了模拟跟车行为，根据领头车的运动设计了三种跟车情况。基于风险稳态理论，提取了七个跟车指标。然后，熵权法用于将选定的指标整合为一个指标，以表示驾驶员的感知风险。应用多元线性回归法测量不利天气条件对感知风险的影响，并将系数视为指标。结果表明，天气条件和道路类型均对跟车行为产生重大影响。随着天气状况的恶化，驾驶员的感知风险趋于增加。在可见性极差的情况下，例如浓雾，由于车辆操作困难和可见性有限，被测驾驶员的感知风险较低。并将系数视为指标。结果表明，天气条件和道路类型均对跟车行为产生重大影响。随着天气状况的恶化，驾驶员的感知风险趋于增加。在可见性极差的条件下，例如浓雾，由于车辆操作困难和可见度有限，被测驾驶员的感知风险较低。并将系数视为指标。结果表明，天气条件和道路类型均对跟车行为产生重大影响。随着天气状况的恶化，驾驶员的感知风险趋于增加。在可见性极差的条件下，例如浓雾，由于车辆操作困难和可见度有限，被测驾驶员的感知风险较低。