With the continuous improvement of automated vehicles, researchers have found that automated driving is more likely to cause passive fatigue. To explore the impact of automation and scenario complexity on the passive fatigue of a driver, we collected electroencephalography (EEG), detection-response task (DRT) performance, and the subjective report scores of 48 drivers. We found that in automated driving under monotonic conditions, after 40 min, the alpha power of the driver’s EEG indicators increased significantly, the accuracy of the detection reaction task decreased, and the reaction time became slower. The receiver characteristic curve was used to calculate the critical threshold of the alpha power during passive fatigue. The determination of the threshold further clarifies the occurrence time and physiological characteristics of passive fatigue and improves the passive fatigue theory.

Previous studies have shown that male drivers drive faster than female drivers, but there is no agreement on whether impulsivity could induce this sex difference, nor is there a cross-sectional comparison of the effects of different road environments. The purpose of this study was to verify whether impulsivity and impulse control could explain the sex differences in driving speed. A driving simulator study (study 1, N = 41) was performed to investigate whether there were sex differences in driving speeds in two road sections of different complexity, and a questionnaire survey (study 2, N = 163) was conducted to investigate the relationship between sex, impulsivity, impulse control and driving behavior of the participants. The results showed that male drivers drove faster on simple roads, but this difference did not show on complex roads. There were no sex differences in impulsivity traits, but male participants had significant lower levels of impulse control. The results also reveal a partial mediating role of impulse control in the relationship between sex and driving speed. These results suggest that impulse control can predict dangerous driving behaviors and is an important factor in explaining sex differences in driving speed.

With ongoing improvements in vehicle automation, research on automation trust has attracted considerable attention. In order to explore effects of automation trust on drivers’ visual distraction, we designed a three-factor 2 (trust type: high trust group, low trust group) × 2 (video entertainment: variety-show videos, news videos) × 3 (measurement stage: 1–3) experiment. 48 drivers were recruited in Dalian, China for the experiment. With a driving simulator, we used detection-response tasks (DRT) to measure each driver’s performance. Their eye movements were recorded, and automation-trust scale was used to divide participants into high trust group and low trust group. The results show that: (1) drivers in the high trust group has lower mental workload and paid more attention to visual non-driving-related tasks; (2) video entertainment also has an impact on distraction behavior, variety-show videos catch more attention than news videos. The findings of the present study indicate that drivers with high automation trust are more likely to be involved in non-driving-related visual tasks.

The countermeasure of driver fatigue is valuable for reducing the risk of accidents caused by vigilance failure during prolonged driving. Listening to the radio (RADIO) has been proven to be a relatively effective “in-car” countermeasure. However, the connectivity analysis, which can be used to investigate its alerting effect, is subject to the issue of signal mixing. In this study, we propose a novel framework based on clustering and entropy to improve the performance of the connectivity analysis to reveal the effect of RADIO to maintain driver alertness. Regardless of reducing signal mixing, we introduce clustering algorithm to classify the functional connections with their nodes into different categories to mine the effective information of the alerting effect. Differential entropy (DE) is employed to measure the information content in different brain regions after clustering. Compared with the Louvain-based community detection method, the proposed method shows more superior ability to present RADIO effectin confused functional connection matrices. Our experimental results reveal that the active connection clusters distinguished by the proposed method gradually move from frontal region to parieto-occipital regionwith the progress of fatigue, consistent with the alpha energy changes in these two brain areas. The active class of the clusters in parieto-occipital region significantly decreases and the most active clusters remain in the frontal region when RADIO is taken. The estimation results of DE confirm the significant change (p < 0.05) of information content due to the cluster movements. Hence, preventing the movement of the active clusters from frontal region to parieto-occipital region may correlate with maintaining driver alertness. The revelation of alerting effect is helpful for the targeted upgrade of fatigue countermeasures.

As automated vehicles become more common, there is a need for precise measurement and definition of when and in what ways a driver can use a mobile phone in L2 autonomous driving mode, for how long it can be used, the complexity of the call content, and the accumulated mental workload. This study uses a 2 (driving mode) × 2 (call content complexity) × 6 (driving stage) three-factor mixed experimental design to investigate the effect of these factors on the driver's mental workload by measuring the driver's performance on Detection response tasks, pupil diameter, and EEG components in various brain regions in the alpha band. The results showed that drivers' mental workload levels converge between manual and automatic driving modes as the duration of driving increases, regardless of the level of complexity of the mobile phone conversation. This suggests that mobile phone conversations can also disrupt the driver's cognitive resource balance in L2 automatic driving mode, as it increases mental workload while also impairing the normal functioning of brain functions such as cognitive control, problem solving, and judgment, thereby compromising driving safety.