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Inflating system expectations prior to SAE level 3 automated vehicle use: effects on monitoring behavior, resumption of control, and attitudes toward driving …

Increasing levels of vehicle automation bring new risks to drivers, particularly those who are using a new automated driving system (ADS). The overreliance on partially automated advanced driver assistance systems (SAE L2 ADAS) has led to crashes, a concern that might escalate with conditional ADS (SAE L3), which require timely driver intervention. This study examines the impact of how L3 ADS’ capabilities and limitations are communicated to users on their subjective attitudes toward ADS and their driving behavior and performance, particularly concerning safety. Method In a driving simulator study, participants received introductory videos about the role of drivers at different automation levels, the capabilities and limitations of L3 ADS, and its human–machine interface (HMI). Videos concluded with either “Highlighted Benefits” of higher automation levels, reflecting current marketing trends of ADAS and ADS, or an “Explicit Reminder” of driver responsibilities in L3 ADS usage. Participants then completed three driving simulator runs (repeated measures) during or after which visual monitoring behavior, take-over performance, and subjective attitudes (trust and acceptance) toward the ADS were gathered. Results Participants resumed control when receiving uncertainty alert from the ADS across both introductory information conditions, with minor differences in take-over performance and monitoring behavior. No significant differences were observed in road monitoring behavior, take-over performance, and subjective attitudes between conditions, except for subjective familiarity ratings, which decreased over runs for the Explicit Reminder group compared to the Highlighted Benefits group. Both conditions showed take-over performance improvements, particularly after practice. Conclusions Successful crash avoidance in both groups indicates that graded warnings and practice can effectively improve take-over performance. This similarity in outcomes suggests that introductory information about ADS may not significantly affect monitoring behavior and performance of ADS users. These results highlight the potential for such systems to mitigate ADS complacency and promote safer resumption of vehicle control during automation failures.

2 versions available

Informing Drivers of ADAS Capability: Effects of Functionality vs. Limitation-Focused Training on Takeover in Silent Failure Scenarios

Year: 2025

Authors: S Yan, J Zhang, Z Wang, D He , Thrust of Robotics and Autonomous Systems, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China, Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China, HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Shenzhen, China

As advanced driver assistance systems evolved to incorporate more complex features, enhancing drivers’ understanding of the ADAS capability becomes crucial for ensuring safe and effective human-automation collaboration in critical system failure situations. However, although academia has emphasized the importance of informing drivers about the limitations of ADAS, most ADAS information conveyed in the sales channel remains basic or is mainly positive, highlighting the functions rather than the limitations of the ADAS. Thus, it is essential to comprehend how such positive information influences users’ perception of ADAS and whether additional limitation information enhances or hinders drivers’ understanding of ADAS. Further, most previous studies validated the effects of training in system failures with takeover requests (TORs), which are different from real-world scenarios, where silent failures without TORs dominate. Thus, this study investigates the effectiveness of various training strategies in conveying ADAS capabilities (including functionality-focused, limitation-focused, and combined training) to drivers. In a driving simulator experiment with 32 participants, we evaluated how drivers behave in silent failure scenarios after receiving different training programs. The results show that functionality-focused training induces hesitation in critical scenarios, while limitation-focused training encourages vigilance but triggers over-reactions during takeover events. In contrast, combined training significantly enhances situational awareness and takeover performance without increasing mental workload, underscoring the importance of a balanced educational approach. These findings highlight the role of driver training, even in silent failures, emphasizing the need for comprehensive training that integrates both system functionalities and limitations and provide insights for optimizing human-automation collaboration in safety-critical scenarios.

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Investigating effects of temperature and CO2 on driver drowsiness in the context of conditional automated driving

With the introduction of conditional automated driving, drivers are freed from continuous control but must remain alert for takeover requests. This study examines how in-cabin temperature (22.5 °C, 25 °C and 27.5 °C) and CO2 (4200 and 1200 ppm) influence driver drowsiness and physiological responses under conditional automated driving. A driving simulator experiment involving 60 participants was conducted, collecting subjective ratings, eye-tracking and physiological data. Results showed that cooler temperatures were associated with lower drowsiness levels compared to neutral temperatures. However, physiological responses may mainly reflect thermoregulation when temperature varies, obscuring drowsiness-related changes. Further, although CO2 concentration did not significantly affect subjective drowsiness, higher CO2 levels attenuated cardiovascular and autonomic activity, suggesting CO2 effects on physiological responses can emerge before conscious awareness. These findings suggest that climate control systems in automated vehicles should balance comfort, efficiency and driver alertness, while physiology-based driver monitoring systems should incorporate environmental data to detect drowsiness earlier.

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Modulating driver visual behavior in highway tunnel groups through rhythm-based visual cues

Drivers navigating highway tunnel groups face complex and repetitive lighting environments, which increase cognitive load and compromise safety. This study aimed to evaluate the effectiveness of rhythm-based visual cues in improving perceptual orientation and regulating driver visual behavior in tunnel group scenarios. Methods To determine the most suitable rhythm pattern for visual guidance, a rhythm adaptability selection process was first conducted using a fuzzy evaluation method. A simulated driving experiment was then performed in UC-win/Road, exposing participants to varying lighting conditions with and without rhythm-based visual cues. Driver eye-movement metrics, including fixation duration, saccade amplitude, and saccade velocity, were used as behavioral indicators to assess visual load and attention regulation. Results Extended tunnel driving was associated with increased fixation durations, a dominance of small-angle saccades (0°–10°), and slower saccade velocities—patterns indicative of visual fatigue and decreased attentional control. The introduction of rhythm-based visual cues significantly improved visual behavior by reducing fixation durations, lowering the frequency of small-angle saccades, and enhancing saccade dynamics. These improvements were more prominent under low-light conditions, indicating better visual adaptability and engagement. Conclusions Rhythm-based visual cues provide an effective and energy-efficient alternative to increased tunnel illumination by directly targeting visual behavior. The findings support the application of perceptually informed environmental design strategies that utilize rhythm sensitivity to improve driver safety in highway tunnel groups.

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Neurofeedback training on motor performance and gaze behavior of skilled pistol shooters

Introduction and Objectives The aim of the present study was to investigate the effectiveness of neurofeedback training on motor performance and gaze behavior (eye rest time) of skilled shooters. Materials and Methods The research was a quasi-experimental and cross-sectional study, and the research design included an experimental group and a control group, including pre-test, post-test, and two-month follow-up. Among professional shooters in Tehran province, 30 people aged 18 to 29 years were randomly assigned to two groups of 15 people. The neurofeedback training group also participated in neurofeedback training while participating in shooting training, which consisted of 10 30-minute sessions, and the protocol included alpha suppression with a frequency band of 8 to 12 Hz and at the F4 position for each subject. The Ergonier eye tracking device was used to record the resting eye duration, and the standard air rifle target test was used to assess the shooters' motor performance. The scores were recorded directly using special forms. Finally, the data were analyzed in SPSS software and with appropriate statistical tests. Findings The results of the study showed that the neurofeedback training group showed a significant increase in the motor performance variable and resting eye duration (milliseconds) (p<0.05). In the shooters' motor performance variable, the comparison of the groups in the post-test stage showed a significant difference between the control group and the neurofeedback training group (p<0.05). Conclusion: In summary, the findings of the present study show that neurofeedback training increases the performance of skilled shooters, but the control group did not show any significant changes from the pre-test to the two-month follow-up test; therefore, the superiority of neurofeedback training in motor performance and quiet eye time is the final result of the present study.

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Operational performance, cognitive load, visual attention, and usability of fixed-, manual-, and autonomous-camera control in single-and multiple-camera …

Camera control is crucial in telemanipulation, yet its effects on human operators remain underexplored. This study examined five camera viewpoint control models in a telemanipulated cube-stacking task involving 35 participants: (1) three fixed cameras; (2) two fixed cameras plus one dynamic camera with autonomous viewpoint control; (3) two fixed cameras plus one dynamic camera with manual viewpoint control; (4) a single dynamic camera with autonomous control; and (5) a single dynamic camera with manual control. We evaluated performance (cube-stacking success rate and completion time), cognitive load (eye-tracking measures of blink rate and pupillary activity, and perceived workload), visual attention (eye-tracking measures of fixation and saccade rates), and usability. Multiple-camera models improved task success but increased cognitive load (lower blink rates and higher pupillary activity) and saccade rates. Between multiple-camera models, autonomous-camera models showed lower saccade rates. Dynamic-camera models were rated more usable than fixed cameras. These findings reveal key trade-offs in camera control design and guide the creation of more efficient, operator-friendly telemanipulation systems.

2 versions available

Overview of optical radiation safety requirements for eye tracking systems

Eye-tracking systems have gained significant attention in various applications, including human-computer interaction, and direct eye projection applications such as near-to-eye displays and augmented/virtual/mixed reality. These systems use optical sensors to monitor eye movements and gaze direction in order to adapt and optimize the displayed image. The working principles of these sensors are based on the emission and detection of optical radiation of light emitting diodes or lasers. Therefore, the optical radiation safety needs to be considered during development of such eye-tracking systems to enable safe use. The aim of the paper is to provide an overview of existing standards and technical specifications related to eye tracking and to highlight potential gaps. As a result, it contributes to the responsible and safe deployment of eye-tracking technology across various applications.

2 versions available

Personalized Course Recommendations Leveraging Machine and Transfer Learning Toward Improved Student Outcomes

University advising at matriculation must operate under strict information constraints, typically without any post-enrolment interaction history.We present a unified, leakage-free pipeline for predicting early dropout risk and generating cold-start programme recommendations from pre-enrolment signals alone, with an optional early-warning variant incorporating first-term academic aggregates. The approach instantiates lightweight multimodal architectures: tabular RNNs, DistilBERT encoders for compact profile sentences, and a cross-attention fusion module evaluated end-to-end on a public benchmark (UCI id 697; n = 3630 students across 17 programmes). For dropout, fusing text with numerics yields the strongest thresholded performance (Hybrid RNN–DistilBERT: f1-score ≈ 0.9161, MCC ≈ 0.7750, and simple ensembling modestly improves threshold-free discrimination (Area Under Receiver Operating Characteristic Curve (AUROC) up to ≈0.9488). A text-only branch markedly underperforms, indicating that numeric demographics and early curricular aggregates carry the dominant signal at this horizon. For programme recommendation, pre-enrolment demographics alone support actionable rankings (Demographic Multi-Layer Perceptron (MLP): Normalized Discounted Cumulative Gain @ 10 (NDCG@10) ≈ 0.5793, Top-10 ≈ 0.9380, exceeding a popularity prior by 25–27 percentage points in NDCG@10); adding text offers marginal gains in hit rate but not in NDCG on this cohort. Methodologically, we enforce leakage guards, deterministic preprocessing, stratified splits, and comprehensive metrics, enabling reproducibility on non-proprietary data. Practically, the pipeline supports orientation-time triage (high-recall early-warning) and shortlist generation for programme selection. The results position matriculation-time advising as a joint prediction–recommendation problem solvable with carefully engineered pre-enrolment views and lightweight multimodal models, without reliance on historical interactions.

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Physdrive: A multimodal remote physiological measurement dataset for in-vehicle driver monitoring

Robust and unobtrusive in-vehicle physiological monitoring is crucial for ensuring driving safety and user experience. While remote physiological measurement (RPM) offers a promising non-invasive solution, its translation to real-world driving scenarios is critically constrained by the scarcity of comprehensive datasets. Existing resources are often limited in scale, modality diversity, the breadth of biometric annotations, and the range of captured conditions, thereby omitting inherent real-world challenges in driving. Here, we present PhysDrive, the first large-scale multimodal dataset for contactless in-vehicle physiological sensing with dedicated consideration on various modality settings and driving factors. PhysDrive collects data from 48 drivers, including synchronized RGB, near-infrared camera, and raw mmWave radar data, accompanied with six synchronized ground truths (ECG, BVP, Respiration, HR, RR, and SpO2). It covers a wide spectrum of naturalistic driving conditions, including driver motions, dynamic natural light, vehicle types, and road conditions. We extensively evaluate both signal-processing and deep-learning methods on PhysDrive, establishing a comprehensive benchmark across all modalities, and release full open-source code with compatibility for mainstream public toolboxes. We envision PhysDrive will serve as a foundational resource and accelerate research on multimodal driver monitoring and smart-cockpit systems.

1 version available:

Real-time detection method of angry driving behavior based on bracelet data

Year: 2025 | Published by: 1.Key Laboratory of Automotive Transportation Safety Assurance Technology for Transportation Industry，Chang'an University，Xi'an 710064，China 2.School of Automobile，Chang'an University，Xi'an 710064，China

Authors: Shi-feng NIU(1,2),Shi-jie YU(2),Yan-jun LIU(2),Chong MA(2)

A method for detecting drivers' angry driving behavior has been designed using widely used popular smart bracelet， which provides a new way and method for effective monitoring of angry driving behavior. 50 drivers were recruited to conduct a simulated driving experiment， and a simulated driving scene that caused anger was designed. Then， heart rate index HR and eight heart rate variability （HRV） indexes such as RR.mean， SDNN， RMSSD， PNN50， SDSD， HF， LF and LF/HF obtained from bracelet collection data were used to study the correlation between the acquisition indexes and the angry driving behavior， and screen the significant influence indexes Finally， using three methods， namely support vector machine （SVM）， K-nearest neighbor （KNN） and linear discriminant analysis （LDA）， established and verified the detection model of angry driving behavior. The results show that the model based on KNN algorithm has the best performance on anger recognition. The accuracy of anger intensity recognition can reach 75%， and the accuracy of anger state recognition is 86 %. The results show that the wearable device （smart bracelet） can reasonably detect the driver 's anger state and anger intensity. Key words: vehicle application engineering, anger driving behavior, machine learning, smart bracelet, heart rate variability

1 version available: Journal of Jilin University(Engineering and Technology Edition)

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Inflating system expectations prior to SAE level 3 automated vehicle use: effects on monitoring behavior, resumption of control, and attitudes toward driving …

Informing Drivers of ADAS Capability: Effects of Functionality vs. Limitation-Focused Training on Takeover in Silent Failure Scenarios

Investigating effects of temperature and CO2 on driver drowsiness in the context of conditional automated driving

Modulating driver visual behavior in highway tunnel groups through rhythm-based visual cues

Neurofeedback training on motor performance and gaze behavior of skilled pistol shooters

Operational performance, cognitive load, visual attention, and usability of fixed-, manual-, and autonomous-camera control in single-and multiple-camera …

Overview of optical radiation safety requirements for eye tracking systems

Personalized Course Recommendations Leveraging Machine and Transfer Learning Toward Improved Student Outcomes

Physdrive: A multimodal remote physiological measurement dataset for in-vehicle driver monitoring

Real-time detection method of angry driving behavior based on bracelet data

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