National Transport Design Centre, Centre for Future Transport and Cities, Coventry University, Coventry, UK.
School of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Cranfield, UK.
Hum Factors. 2024 Sep;66(9):2244-2263. doi: 10.1177/00187208231185705. Epub 2023 Jun 26.
Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving.
Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers' states posits it could become a novel method for measuring risk perception.
Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports.
Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario.
This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario.
Little is known regarding the effects of drivers' risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology.
利用大脑血液动力学反应来测量自动驾驶中感知到的交通复杂性风险。
尽管在手动驾驶中已经得到了很好的证实，但在自动驾驶中驾驶员风险感知的影响仍然未知。本文使用 fNIRS 来评估驾驶员的状态，这可能成为一种测量风险感知的新方法。
23 名志愿者参与了具有自动驾驶功能的实证驾驶模拟器实验。驾驶条件涉及到具有不同交通复杂性水平的郊区和城市场景，最终出现了意外的危险事件。通过 fNIRS 在大脑前额皮质的血红蛋白氧合作用和自我报告来测量感知风险。
自我报告的感知风险和交通复杂性增加后，大脑前额皮质的血红蛋白氧合水平显著增加，特别是在危险场景中。
本文证明了 fNIRS 是一种测量高度自动驾驶中感知到的交通复杂性风险变化的有价值的研究工具。即使驾驶任务的责任委托给自动化系统，且处置信任度很高，当交通复杂性逐渐增加时，驾驶员仍会感知到中等风险，这反映在血液氧合水平的相应显著增加上，主观（自我报告）和客观（fNIRS）在危险场景中进一步增加。
对于自动化驾驶中驾驶员风险感知的影响知之甚少。在我们的实验结果的基础上，未来的工作可以使用 fNIRS 来研究风险评估的心理过程以及感知风险对驾驶行为的影响，以促进自动化驾驶技术的安全采用。
