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Takeover Quality: Assessing the Effects of Time Budget and Traffic Density with the Help of a Trajectory-Planning Method
Journal of Advanced Transportation ( IF 2.0 ) Pub Date : 2020-07-01 , DOI: 10.1155/2020/6173150
Fabian Doubek 1, 2 , Erik Loosveld 1, 2 , Riender Happee 1 , Joost de Winter 1
Affiliation  

In highly automated driving, the driver can engage in a nondriving task but sometimes has to take over control. We argue that current takeover quality measures, such as the maximum longitudinal acceleration, are insufficient because they ignore the criticality of the scenario. This paper proposes a novel method of quantifying how well the driver executed an automation-to-manual takeover by comparing human behaviour to optimised behaviour as computed using a trajectory planner. A human-in-the-loop study was carried out in a high-fidelity 6-DOF driving simulator with 25 participants. The takeover required a lane change to avoid roadworks on the ego-lane while taking other traffic into consideration. Each participant encountered six different takeover scenarios, with a different time budget (5 s, 7 s, or 20 s) and traffic density level (low or medium). Results showed that drivers exhibited a considerably higher longitudinal and lateral acceleration than the optimised behaviour, especially in the short time budget scenarios. In scenarios of medium traffic density, the trajectory planner showed a moderate deceleration to let a vehicle in the left lane pass; many participants, on the other hand, did not decelerate before making a lane change, resulting in a dangerous emergency brake of the left-lane vehicle. In conclusion, our results illustrate the value of assessing human takeover behaviour relative to optimised behaviour. Using the trajectory planner, we showed that human drivers are unable to behave optimally in urgent scenarios and that, in some conditions, a medium deceleration, as opposed to a maximal or minimal deceleration, is optimal.

中文翻译:

接管质量:借助轨迹规划方法评估时间预算和交通密度的影响

在高度自动化的驾驶中,驾驶员可以从事非驾驶任务,但有时必须接管控制。我们认为,当前的接管质量衡量标准,例如最大纵向加速度,是不够的,因为它们忽略了方案的关键性。本文提出了一种新颖的方法,该方法通过将人的行为与使用轨迹规划器计算出的优化行为进行比较,来量化驾驶员执行自动化到手动接管的程度。在25位参与者的高保真6自由度驾驶模拟器中进行了在车中的人体研究。接管需要改变车道,以避免在我行车道上进行修路,同时考虑其他交通。每个参与者都遇到了六个不同的接管方案,并且时间预算不同(5秒,7秒,或20秒)和交通密度等级(低或中)。结果表明,驾驶员表现出的纵向和横向加速度要比优化的行为高得多,尤其是在短时间预算情况下。在中等交通密度的情况下,轨迹规划器显示出适度的减速,以使左侧车道中的车辆通过。另一方面,许多参与者在改变车道之前没有减速,从而导致左侧车道的紧急制动危险。总之,我们的结果说明了评估人类接管行为相对于最佳行为的价值。使用轨迹规划器,我们显示了人类驾驶员在紧急情况下无法表现出最佳表现,并且在某些情况下,相对于最大或最小减速度,中等减速度是最佳的。结果表明,驾驶员表现出的纵向和横向加速度要比优化的行为高得多,尤其是在短时间预算情况下。在中等交通密度的情况下,轨迹规划器显示出适度的减速,以使左侧车道中的车辆通过。另一方面,许多参与者在改变车道之前没有减速,从而导致左侧车道的紧急制动危险。总之,我们的结果说明了评估人类接管行为相对于最佳行为的价值。使用轨迹规划器,我们显示了人类驾驶员在紧急情况下无法表现出最佳表现,并且在某些情况下,相对于最大或最小减速度,中等减速度是最佳的。结果表明,驾驶员表现出的纵向和横向加速度要比优化的行为高得多,尤其是在短时间预算情况下。在中等交通密度的情况下,轨迹规划器显示出适度的减速,以使左侧车道中的车辆通过。另一方面,许多参与者在改变车道之前没有减速,从而导致左侧车道的紧急制动危险。总之,我们的结果说明了评估人类接管行为相对于最佳行为的价值。使用轨迹规划器,我们显示了人类驾驶员在紧急情况下无法表现出最佳表现,并且在某些情况下,相对于最大或最小减速度,中等减速度是最佳的。
更新日期:2020-07-01
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