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The driver’s perception level and takeover performance are two major factors that result in accidents in autonomous vehicles. This study’s goal is to analyze the change in drivers’ perception level and its influence on takeover performance during autonomous driving. A takeover behavior test platform is implemented based on a high-fidelity driving simulator. The fog zone is selected as the takeover scenario. Thus, a 2 (takeover request time: 5 s, 10 s) by 2 (non-driving-related task: work task, entertainment task) takeover experiment was conducted. A generalized linear mixed model is developed to explore the influence of the perception level on takeover performance. The study finds out that, after the takeover request is triggered, the driver’s gaze duration is shortened and the pupil area is enlarged, which is helpful for the driver to extract and understand the road information faster. Male drivers have greater perception levels than female drivers, and they prioritize leisure tasks more than professional ones. The drivers’ perception level decreases when age increases. The shorter the gaze duration is, and the larger the pupil area is, the shorter the takeover response time will be. In addition, drivers’ perception level has a positive effect on takeover performance. Finally, this study provides a reference for revealing the changing rules of drivers’ perception level in autonomous driving, and the study can provide support for the diagnosis of takeover risks of autonomous vehicles from the perspective of human factors.

This study explores the associations between crash/near-crash (C/NC) events and roadway, driver-related, and environmental factors in naturalistic driving studies (NDS). We used the Naturalistic Engagement in Secondary Tasks (NEST) dataset, which is massive and detailed and contains 50 million miles of naturalistic driving data resulting from the Strategic Highway Research Program 2 (SHRP2). Association rule mining (ARM) is applied to extract the rules for frequently occurring events. The generated association rules are filtered by four metrics (support, confidence, lift, and conviction) and validated by the lift increase criterion. A three-step analysis is performed to obtain a comprehensive understanding of the rules of C/NC events. The 20 most frequent items are first selected to investigate their relationship with the C/NC events. Subsequently, the association rules are used to identify the factors contributing to C/NC events. Finally, correlations between contributing factors and different severities of crashes (I—most severe, II—police-reportable, III—minor crash, and IV—low-risk tire strike) are analyzed by ARM. The results demonstrate that C/NC events occur most frequently on straight and level road segments with no controlled intersections or traffic control devices when drivers are performing secondary tasks. Thus, the reasons for these crashes are carelessness and overconfidence. In addition, a median strip or barrier and a wider road can significantly reduce the frequency and severity of crash events. Moreover, gender, age, average annual mileage, and secondary tasks are highly correlated with the frequency and severity of C/NC events. Drivers with visual-spatial disabilities or crash records are more likely to be involved in the most severe crash events. Near-crash events occur more frequently at higher traffic density and on roads with traffic control devices and controlled intersections. These conditions may keep drivers alert, preventing crashes.

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