论文标题
对有条件自动驾驶的接管请求的心理生理反应
Psychophysiological responses to takeover requests in conditionally automated driving
论文作者
论文摘要
在SAE 3级自动驾驶中,接管自动化的控制引起了重大的安全问题,因为从车辆控制循环中的驾驶员很难谈判接管过渡。现有关于接管过渡的研究集中在驾驶员对接管请求(TOR)的行为反应上。作为补充,这项探索性研究旨在检查驾驶员对TOR的心理生理反应,这是由于不同的非驾驶相关任务(NDRT),交通密度和TOR的交付时间。总共招募了102名驾驶员,每个驾驶员都在高保真固定式驾驶模拟器中经历了8个接管事件。在两个阶段记录并分析了驾驶员的凝视行为,心率(HR)活动,电流皮肤反应(GSR)和面部表情。首先,在自动驾驶阶段,我们发现驾驶员的心率变异性较低,水平注视分散量较窄,并且在公路上的眼睛较短时,相对于认知量较低的认知负荷水平很高。其次,在接管过渡阶段,4S提前时间导致眨眼数,最大和平均GSR阶段性激活比7s的交付时间更大,而交通密度较重,而交通密度较重,而与光交通密度相比,HR加速度增加了。我们的结果表明,心理生理措施可以表明驾驶员的特定内部状态,包括他们的工作量,情绪,注意力和处境意识,并以连续的,无创的和实时的方式。这些发现为在自动驾驶中使用心理生理学测量的价值以及在驾驶员监控系统和自适应警报系统中的应用提供了更多支持。
In SAE Level 3 automated driving, taking over control from automation raises significant safety concerns because drivers out of the vehicle control loop have difficulty negotiating takeover transitions. Existing studies on takeover transitions have focused on drivers' behavioral responses to takeover requests (TORs). As a complement, this exploratory study aimed to examine drivers' psychophysiological responses to TORs as a result of varying non-driving-related tasks (NDRTs), traffic density and TOR lead time. A total number of 102 drivers were recruited and each of them experienced 8 takeover events in a high fidelity fixed-base driving simulator. Drivers' gaze behaviors, heart rate (HR) activities, galvanic skin responses (GSRs), and facial expressions were recorded and analyzed during two stages. First, during the automated driving stage, we found that drivers had lower heart rate variability, narrower horizontal gaze dispersion, and shorter eyes-on-road time when they had a high level of cognitive load relative to a low level of cognitive load. Second, during the takeover transition stage, 4s lead time led to inhibited blink numbers and larger maximum and mean GSR phasic activation compared to 7s lead time, whilst heavy traffic density resulted in increased HR acceleration patterns than light traffic density. Our results showed that psychophysiological measures can indicate specific internal states of drivers, including their workload, emotions, attention, and situation awareness in a continuous, non-invasive and real-time manner. The findings provide additional support for the value of using psychophysiological measures in automated driving and for future applications in driver monitoring systems and adaptive alert systems.