Di Flumeri Gianluca, Borghini Gianluca, Aricò Pietro, Sciaraffa Nicolina, Lanzi Paola, Pozzi Simone, Vignali Valeria, Lantieri Claudio, Bichicchi Arianna, Simone Andrea, Babiloni Fabio
BrainSigns srl, Rome, Italy.
IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Rome, Italy.
Front Hum Neurosci. 2018 Dec 18;12:509. doi: 10.3389/fnhum.2018.00509. eCollection 2018.
Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver's behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver's workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver's perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers' behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers' behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research.
驾驶汽车被认为是一项非常复杂的活动，它由不同的伴随任务和子任务组成，因此了解不同因素（如道路复杂性、交通状况、仪表盘设备和外部事件）对驾驶员行为和表现的影响至关重要。因此，在特定情况下，驾驶员所经历的认知需求可能非常高，会导致过度的心理工作量，进而增加犯错的概率。在这方面，已经证明人为失误是57%的道路事故的主要原因，并且在大多数事故中都是一个促成因素。在这项研究中，20名年轻受试者参与了一项实际驾驶实验，实验在不同交通状况（高峰时段和非高峰时段）以及不同道路类型（主干道和次干道）下进行。此外，在驾驶任务期间，还设置了不同的特定事件，特别是有一名行人过马路以及一辆汽车刚好在实验对象前方进入车流。基于驾驶员脑电图（EEG）即大脑活动的工作负荷指数被用来研究不同因素对驾驶员工作负荷的影响。还采用了眼动追踪（ET）技术和主观测量方法，以便全面了解驾驶员感知到的工作负荷，并研究从所采用方法中可获得的不同见解。基于EEG的工作负荷指数的应用证实了交通状况和道路类型对驾驶员行为都有显著影响（增加了他们的工作负荷），其优势在于处于真实场景中。此外，它还能够突出驾驶过程中与外部事件相关的工作负荷增加情况，特别是在交通流量较低的情况下有显著影响。最后，方法之间的比较显示神经生理测量相对于ET和主观测量具有更高的灵敏度。总之，这样一个基于EEG的工作负荷指数能够客观地评估驾驶员所经历的心理工作负荷，作为一种强大的工具脱颖而出，可用于旨在研究驾驶员行为的研究，并为道路安全研究中使用的传统方法提供额外的补充见解。
