自然驾驶状态下重型工程车驾驶人驾驶稳定性分析

重型工程车行驶过程中事故风险大,发生恶性事故的概率高,易造成重大生命和经济损失,其运输安全管理问题面临挑战.为探究重型工程车驾驶人驾驶稳定性与相关影响因素之间的关系,开展重型工程车自然驾驶试验,提取车辆运动学、道路条件、驾驶人状态和工作时间等数据;采用速度均值和速度标准差表征驾驶人驾驶稳定性,以睡眠模式、道路线形、道路...

Analysis of Driving-stability Factors for Heavy-duty Truck Drivers Under Naturalistic Driving Conditions

Heavy-duty trucks have a high risk of accidents and a high probability of serious accidents. These can easily cause major life and economic losses. The transportation-safety management of heavy-duty trucks is challenging. To explore the relationship between the driving stability of heavy-duty truck drivers and related influencing factors, this study conducted a natural driving experiment with heavy-duty truck drivers to extract data, such as vehicle kinematics, road conditions, driver status, and working hours. The average speed and standard deviation were used to characterize the driving stability of the driver, and the sleep pattern, road alignment, road type, intersection influencing area, load capacity and working time were used as explanatory variables. Two generalized linear mixed models (GLMMs) were constructed, considering the heterogeneity of individual drivers. After comparing with the linear model, the results show that random effects should be considered in the speed-mean model. The sleep mode, road alignment, road type, and load have a significant impact on the mean speed. Drivers with poor sleep modes tend to drive at a lower speed. The speed standard-deviation model excludes the heterogeneity of individual drivers. The road alignment, intersection influencing area, road type, load capacity, and working time have a significant influence on the standard deviation of speed. To improve the driving stability of heavy-duty truck drivers, it is recommended that drivers form healthy and good sleep patterns, pay attention to the potential risks caused by road-alignment changes, become more cautious when passing through intersection influencing areas, and drive with no load during the day. These research results can provide a theoretical basis for correcting dangerous driving behaviors and the work-scheduling management of heavy-duty construction-truck drivers.