With increasing automation, drivers' role progressively transitions from active operators to passive system supervisors, affecting their behaviour and cognitive processes. This study aims to understand better attention allocation and perceived cognitive load in manual, L2, and L3 driving in a realistic environment. We conducted a test-track experiment with 30 participants. While driving a prototype automated vehicle, participants were exposed to a passive auditory oddball task and their EEG was recorded. We studied the P3a ERP component elicited by novel environmental cues, an index of attention resources used to process the stimuli. The self-reported cognitive load was assessed using the NASA-TLX. Our findings revealed no significant difference in perceived cognitive load between manual and L2 driving, with L3 driving demonstrating a lower self-reported cognitive load. Despite this, P3a mean amplitude was highest during manual driving, indicating greater attention allocation towards processing environmental sounds compared to L2 and L3 driving. We argue that the need to integrate environmental information might be attenuated in L2 and L3 driving. Further empirical evidence is necessary to understand whether the decreased processing of environmental stimuli is due to top-down attention control leading to attention withdrawal or a lack of available resources due to high cognitive load. To the best of our knowledge, this study is the first attempt to use the passive oddball paradigm outside the laboratory. The insights of this study have significant implications for automation safety and user interface design.

翻译：随着自动化程度的提高，驾驶员的角色逐渐从主动操作者转变为被动系统监督者，这影响了他们的行为和认知过程。本研究旨在更好地理解在真实环境中手动驾驶、L2级自动驾驶和L3级自动驾驶下的注意力分配和感知认知负荷。我们进行了一项包含30名参与者的测试道路实验。在驾驶原型自动驾驶车辆时，参与者暴露于被动听觉Oddball任务中，并记录了其脑电图（EEG）。我们研究了由新异环境线索诱发的P3a事件相关电位（ERP）成分，该成分是用于处理刺激的注意力资源指标。使用NASA-TLX量表评估了自我报告的认知负荷。研究结果显示，手动驾驶与L2级自动驾驶之间的感知认知负荷无显著差异，而L3级自动驾驶表现出较低的自我报告认知负荷。尽管如此，手动驾驶期间的P3a平均幅值最高，表明相较于L2级和L3级自动驾驶，手动驾驶时对环境声音的注意力分配更多。我们认为，在L2级和L3级自动驾驶中，整合环境信息的需求可能有所减弱。需要进一步的实证证据来理解环境刺激处理减少是由于自上而下的注意力控制导致注意力脱离，还是由于高认知负荷导致可用资源不足。据我们所知，本研究是首次在实验室外使用被动Oddball范式的尝试。本研究的见解对自动化安全及用户界面设计具有重要意义。