Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China.
Tongji Architectural Design (Group) Co., Ltd., Shanghai 200092, China.
Int J Environ Res Public Health. 2023 Feb 9;20(4):3069. doi: 10.3390/ijerph20043069.
Nowadays, conditional automated driving vehicles still need drivers to take-over in the scenarios such as emergency hazard events or driving environments beyond the system's control. This study aimed to explore the changing trend of the drivers' takeover behavior under the influence of traffic density and take-over budget time for the entire take-over process in emergency obstacle avoidance scenarios. In the driving simulator, a 2 × 2 factorial design was adopted, including two traffic densities (high density and low density) and two kinds of take-over budget time (3 s and 5 s). A total of 40 drivers were recruited, and each driver was required to complete four simulation experiments. The driver's take-over process was divided into three phases, including the reaction phase, control phase, and recovery phase. Time parameters, dynamics parameters, and operation parameters were collected for each take-over phase in different obstacle avoidance scenarios. This study analyzed the variability of traffic density and take-over budget time with take-over time, lateral behavior, and longitudinal behavior. The results showed that in the reaction phase, the driver's reaction time became shorter as the scenario urgency increased. In the control phase, the steering wheel reversal rate, lateral deviation rate, braking rate, average speed, and takeover time were significantly different at different urgency levels. In the recovery phase, the average speed, accelerating rate, and take-over time differed significantly at different urgency levels. For the entire take-over process, the entire take-over time increased with the increase in urgency. The lateral take-over behavior tended to be aggressive first and then became defensive, and the longitudinal take-over behavior was defensive with the increase in urgency. The findings will provide theoretical and methodological support for the improvement of take-over behavior assistance in emergency take-over scenarios. It will also be helpful to optimize the human-machine interaction system.
如今，条件自动驾驶车辆在紧急危险事件或系统无法控制的驾驶环境等场景中仍需要驾驶员接管。本研究旨在探讨在整个紧急避障场景接管过程中，交通密度和接管预算时间对驾驶员接管行为变化趋势的影响。在驾驶模拟器中，采用了 2×2 析因设计，包括两个交通密度（高密度和低密度）和两种接管预算时间（3 秒和 5 秒）。共招募了 40 名驾驶员，每位驾驶员需要完成四个模拟实验。驾驶员的接管过程分为三个阶段，包括反应阶段、控制阶段和恢复阶段。在不同的避障场景中，为每个接管阶段收集了时间参数、动力学参数和操作参数。本研究分析了交通密度和接管预算时间的可变性与接管时间、横向行为和纵向行为的关系。结果表明，在反应阶段，随着场景紧急程度的增加，驾驶员的反应时间变得更短。在控制阶段，转向轮反转率、横向偏离率、制动率、平均速度和接管时间在不同紧急程度下差异显著。在恢复阶段，在不同紧急程度下，平均速度、加速度和接管时间差异显著。对于整个接管过程，整个接管时间随着紧急程度的增加而增加。横向接管行为先趋于激进，然后变得保守，纵向接管行为随着紧急程度的增加而变得保守。研究结果将为紧急接管场景中接管行为辅助的改进提供理论和方法支持，也有助于优化人机交互系统。
