条件自动驾驶中对接管请求的心理生理反应
Psychophysiological responses to takeover requests in conditionally automated driving
摘要：
在SAE 3级自动驾驶中，从自动驾驶中接管控制会引起严重的安全问题，因为脱离车辆控制回路的驾驶员很难协商接管过渡。现有的研究接管过渡集中在驾驶员的行为反应接管请求（TORs）。作为补充，本探索性研究的目的是检查驾驶员的心理生理反应的TORS作为不同的非驾驶相关的任务（NDRT），交通密度和TOR的前置时间的结果。共招募了102名驾驶员，他们每个人都经历了8个接管事件在高保真固定基地驾驶模拟器。在两个阶段中记录并分析驾驶员的注视行为、心率（HR）活动、皮肤电反应（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，水平凝视分散度较窄，当他们相对于低水平的认知负荷具有高水平的认知负荷时，眼睛在路上的时间较短。第二，在接管过渡阶段，4s的前置时间导致抑制眨眼数和更大的最大和平均GSR相位激活相比，7s的前置时间，而高交通密度导致增加HR加速模式比轻交通密度。我们的研究结果表明，心理生理措施可以指示特定的内部状态的驱动程序，包括他们的工作量，情绪，注意力，并在一个连续的，非侵入性的和实时的方式情况意识。这些发现为在自动驾驶中使用心理生理测量的价值以及未来在驾驶员监控系统和自适应警报系统中的应用提供了额外的支持。
英文摘要：
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.
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