Zhijie Yi

Long-haul truck drivers face a high risk of drowsiness due to extended working hours and monotonous driving conditions. While Advanced Driver Assistance Systems (ADAS) are increasingly adopted, their impact on drowsiness accumulation in real-world heavy-truck operations remains unclear. Furthermore, the reliability of physiological and eye-tracking indicators for drowsiness monitoring in these specific contexts is not well established. Thus, a naturalistic driving study was conducted to analyze how operational timing (day vs. night starts) and driving mode (manual vs. ADAS-supported) influence driver drowsiness and the association between the drowsiness and physiological and eye-tracking indicators. Eight professional truck drivers participated in the study across 16 freight sessions on a 1,550 km route, yielding approximately 155 hours of valid data. Subjective drowsiness (KSS) was recorded hourly and synchronized with ocular, cardiovascular, and respiratory indicators. Mixed-effects models were employed to analyze baseline states, drowsiness trajectories, and indicator associations. Results indicate that night-start sessions began with higher drowsiness levels. Crucially, KSS increased over time during manual driving but remained stable under ADAS. Moreover, the association between physiological indicators (e.g., PERCLOS, RMSSD) and subjective drowsiness was found to be context-dependent, varying significantly with driving mode and operational timing. The study concludes that drowsiness monitoring in long-haul ADAS-supported freight operations must account for specific contextual factors, as standard physiological indicators may not generalize across different driving modes and session-start timings.

This study investigates how pedestrian trust, receptivity, and behavior evolve during interactions with Level-4 autonomous vehicles (AVs) at uncontrolled urban intersections in a naturalistic setting. While public acceptance is critical for AV adoption, most prior studies relied on simplified simulations or field tests. We conducted a real-world experiment in a commercial Robotaxi operation zone, where 33 participants repeatedly crossed an uncontrolled intersection with frequent Level-4 Robotaxi traffic. Participants completed the Pedestrian Behavior Questionnaire (PBQ), Pedestrian Receptivity Questionnaire for Fully AVs (PRQF), pre- and post-experiment Trust in AVs Scale, and Personal Innovativeness Scale (PIS). Results showed that trust in AVs significantly increased post-experiment, with the increase positively associated with the Interaction component of PRQF. Additionally, both the Positive and Error subscales of the PBQ significantly influenced trust change. This study reveals how trust forms in real-world pedestrian-AV encounters, offering insights beyond lab-based research by accounting for population heterogeneity.