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Total results: 274

A study of the effect of monitoring request and takeover request time interval on takeover characteristics

Year: 2025

Authors: H Wan, X Li, J Chen, R Huang, Transportation Safety and Environment, tdaf028

With the advancement of Level-3 conditional automated driving technology, drivers are increasingly engaging in non-driving related tasks (NDRTs) while the vehicle operates autonomously. However, this behavior can reduce takeover performance and compromise driving safety. The monitoring request (MR) mechanism alerts drivers in advance to resume monitoring traffic conditions, thereby improving takeover performance and ensuring safety. This study investigated the effect of four time intervals—3 s, 5 s, 7 s, and 9 s—on driver takeover characteristics, including takeover reaction time, maximum longitudinal deceleration, minimum collision time, fixation count, and the percentage of area of interest (AOI) fixation time. Results showed that longer time intervals significantly enhanced driver takeover performance and situational awareness. In particular, the 7 s and 9 s intervals resulted in significantly better performance and situational awareness compared to the 3 s and 5 s intervals, yet the difference between 7 s and 9 s was not statistically significant. To determine the optimal interval, the CRITIC-TOPSIS evaluation model was applied, which ranked the 7 s interval as the most effective, followed by 9 s, 5 s, and 3 s. These findings indicate that the 7 s interval achieves the best balance between driver preparedness and takeover performance, while the 3 s interval performed the worst. This study provides valuable insights into the design of the time interval between monitoring requests and takeover requests in automated driving systems, contributing to improved driving safety and user satisfaction.

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A systematic and bibliometric review on physiological monitoring systems and wearable sensing devices for mental status monitoring in construction: Trends …

Year: 2025

Authors: D Xu, G Albeaino , ournal of Information Technology in Construction

Advancements in physiological monitoring systems (PMSs) and wearable sensing devices (WSDs) have enabled real-time, objective assessments of workers’ mental status in construction. However, existing studies lack a comprehensive synthesis of mental status monitoring and classification approaches used in construction, including data collection, preprocessing, as well as postprocessing techniques. This paper systematically and bibliometrically reviews 223 studies following PRISMA guidelines, providing a structured framework for PMS and WSD applications in construction. The findings identified ten sensor types used to assess four mental status factors: risk perception, mental workload and fatigue, mental stress, and emotional states. For each sensor, the review details data collection procedures, including sensor brands and models, placements, and sampling rates. Additionally, it examines preprocessing techniques (i.e., noise filtering and artifact removal) and postprocessing methods, including feature extraction and metric computation, data interpretation, as well as mental status classification using rule-based and AI-based methods. Identified limitations and future research directions are also discussed. This study serves as a comprehensive guide for researchers and practitioners, promoting the broader adoption of PMSs and WSDs for mental status monitoring in construction.

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Auton kuljettajan tarkkaamattomuuden ennustaminen silmänliikkeiden avulla

Year: 2025

Authors: V Matarainen - 2025 - jyx.jyu.fi, JYX, Faculty of Information Technology

This study discusses the prediction of car driver inattention using eye movements. The measurements for the study were performed at the University of Jyväskylä’s Drive-In Laboratory. The goal of the study was to find out what kind of glance metrics might be connected to inattention, as measured in the Drive-In laboratory. The research question was how well and with what kind of glance metrics, inattention can be predicted. Data was collected from 48 test subjects who performed tasks related to car functions that interfere with driving in the simulation. Inattention was measured using the HMET system (Head Mounted Eye Tracker). Various glance metrics were collected from the recorded eye movements. Glances were measured between two different Areas of Interest (AOI). The areas were the road and the car’s on-board computer control panel, which is the area where the tasks were performed. Additionally, during the study, it was decided to investigate other explanatory variables related to the research alongside the glance metrics, such as the order of the execution and the number of the task executions within three minutes. This led to creation of two different multi-level models, one of which only eye movement metrics were considered, and in other, variables from outside of the eye movements were also considered. The research resulted in the following result: Decrease in inattention was predicted by a higher number of glances toward the task, a greater total glance time on the road, a longer average duration toward the task and a greater number of execution repetitions within the task time limit. Task performed later predicted increase in inattention. All these connections were significant, but they all had small effect size. Furthermore, the study found that large part of the variance could be explained by differences between the test subjects and the tasks. Possible learning effects and fatigue could be observed in the test subjects as the experimental situation progressed. The topic requires much further research and better data to achieve clearer results. The slightly imprecise measurement method for glance metrics was the biggest weakness of the study’s validity. Keywords: inattention, distraction, eye movement, glance metrics, driving simulation study

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Cabin Environment Matters: Psycho-Physiological Pathways from In-Vehicle Climate to Driver Behaviors in Conditional Automated Driving

Year: 2025

Authors: Z Wang, A Wang, H Sheng, F Gu, L Zhao, The Hong Kong University of Science and Technology(Guangzhou), Department of Civil and Environmental Engineering, The Hong Kong University of Science andTechnology, Hong Kong SAR, China, Thrust of Robotics and Autonomous Systems, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China, HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Guangdong, China

Physical environments shape how humans think, feel, and act, yet their influence on cognition and behavior in automated driving remains underexplored. This study introduces and validates a psycho-physiological framework explaining how in-cabin temperature and CO₂ affect driver behavior during conditional automated driving, based on a high-fidelity driving simulator study with 60 participants. Structural equation modeling was used to test pathways linking environmental factors to driver physiology, subjective states, and takeover performance. Results show that at the psycho-physiological level, slightly cool temperatures reduced drowsiness through direct environmental input but also increased drowsiness via parasympathetic activation and cold discomfort. Slightly warm conditions elevated drowsiness through warm discomfort, underscoring the mediating role of comfort. CO₂ exposure degraded perceived air quality but produced limited downstream effects. At the behavioral level, cool conditions shortened takeover time by reducing drowsiness but impaired post-takeover control through suppressed muscle activation, whereas warm conditions exerted competing influences on takeover time through drowsiness and physiological arousal. These findings advance understanding of how cabin environments shape driver states and behaviors and provide insights for adaptive climate control strategies in human-centered automated vehicles.

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CogMamba: Multi-Task Driver Cognitive Load and Physiological Non-Contact Estimation with Multimodal Facial Features

Year: 2025

Authors: Y Xie, B Guo, Sensors, 2025

The cognitive load of drivers directly affects the safety and practicality of advanced driving assistant systems, especially in autonomous driving scenarios where drivers need to quickly take control of the vehicle after performing non-driving-related tasks (NDRTs). However, existing driver cognitive load detection methods have shortcomings such as the inability to deploy invasive detection equipment inside vehicles and limitations to eye movement detection, which restrict their practical application. To achieve more efficient and practical cognitive load detection, this study proposes a multi-task non-contact cognitive load and physiological state estimation model based on RGB video, named CogMamba. The model utilizes multimodal features extracted from facial video and introduces the Mamba architecture to efficiently capture local and global temporal dependencies, thereby further jointly estimating cognitive load, heart rate (HR), and respiratory rate (RR). Experimental results demonstrate that CogMamba exhibits superior performance on two public datasets and shows excellent robustness under the cross-dataset generalization test. This study provides insights for non-contact driver state monitoring in real-world driving scenarios.

1 version available:

Collision Risk Perception Models Using Physiological and Eye-tracking Signals

Year: 2025

Authors: H Lee, O Lim, A Singh, S Samuel , IEEE Access

Accurate risk perception is essential for safe driving, particularly in dynamic and high-risk traffic environments. This study develops machine learning (ML)-based user risk perception models using physiological recording systems to assess driving risks across various scenarios. We evaluate model performance at different granularity levels, ranging from binary classification (e.g., risk versus no-risk), to detailed classifications, resulting in more classes (e.g., incorporating collision subject types, pedestrian types, numbers, vehicle speeds and deceleration rates). Results demonstrate that despite increased model complexity, the proposed risk perception models consistently achieve high performance, with accuracy exceeding 0.92 in most cases except for high-granularity models in vehicle collision scenarios. Models trained for pedestrian-related risks outperformed those for vehicle-related risks, indicating a stronger physiological response to pedestrian hazards. Feature importance analysis reveals that electroencephalogram (EEG) signals (midline channels from frontal to parietal lobes: Cz, Fz and Pz) play a dominant role, with pupil center shift degree (PCD) and pupil center shift magnitude (PCM) as secondary key contributors. In contrast, features derived from skin temperature and electrodermal activity (EDA) exhibit limited importance due to slower response times. These findings suggest that EEG and pupil features are optimal for real-time risk perception models, with heart activity features serving as complementary factors in enhancing model accuracy and reliability. We also discuss practical applications of these models in driver-vehicle interaction and intelligent transportation systems. By integrating physiological data with environmental perception sensors, these models offer a promising approach to enhancing safety in semi-autonomous driving systems

1 version available:

Could music reduce driver fatigue? An investigation on music effects in various weather conditions

Year: 2025

Authors: H Guo, J Weng, K Shi, L Wang, Journal of Transportation Safety & Securit

Fatigue impairs drivers’ performance and increases the occurrence likelihood of traffic incidents. This study aims to explore the capability of music as an intervention to reduce driver fatigue under different weather conditions by conducting a driving simulation experiment. The Eysenck Personality Questionnaire-Neuroticism (EPQ-N) scale is used to assess drivers’ personalities. A mixed factorial analysis of variance (ANOVA) is applied to analyze the influence of music on the driving behavior of different drivers under various weather conditions, including sunny, rainy, and foggy scenarios. Results indicated that drivers’ fatigue levels are reduced by the music under less complicated weather conditions, such as sunny and foggy conditions. The results also revealed that drivers with low EPQ-N scores could demonstrate better physical reactions and driving performance in sunny and rainy conditions, whereas those with high EPQ-N scores perform better in foggy conditions. These findings could help prevent traffic accidents caused by driving fatigue in sunny and foggy conditions through the strategic use of music.

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Dialogue at the Edge of Fatigue: Personalized Voice Assistant Strategies in Intelligent Driving Systems

Year: 2025

Authors: C Zhou, L Wang, Y Yang , Applied Sciences

With the rapid development of intelligent transportation systems, voice assistants are increasingly integrated into driving environments, providing an effective means to mitigate the risks of fatigued driving. This study explored drivers’ interaction preferences with voice assistants under different fatigue states and proposed a fatigue-state-based dialogue-awakening mechanism. Using Grounded Theory and the Stimulus–Organism–Response (SOR) framework, in-depth interviews were conducted with 25 drivers from diverse occupational backgrounds. To validate the qualitative findings, a driving simulation experiment was carried out to examine the effects of different voice interaction styles on driver fatigue arousal across various fatigue levels. Results indicated that heavily fatigued drivers preferred highly stimulating and interactive voice communication; mildly fatigued drivers tended toward gentle and socially supportive dialogue; while drivers in a non-fatigued state preferred minimal voice interference, activating voice assistance only when necessary. Significant occupational differences were also observed: long-haul truck drivers emphasized practicality and safety in voice assistants, taxi drivers favored voice interactions combining navigation and social content, and private car owners preferred personalized and emotional support. This study enriches the theoretical understanding of fatigue-sensitive voice interactions and provides practical guidance for the adaptive design of intelligent voice assistants, promoting their application in driving safety.

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Driver-Centric Design: Empirical Strategies for Optimizing the Visualization of Driving Information on Vehicle Liquid Crystal Display Dashboards

Year: 2025

Authors: JL Lin, MC Zheng, T Wang, P Liu, IEEE Access, 2025

With the continuous advancement of smart car and vehicle display technologies, liquid crystal display (LCD) dashboards have become the mainstream medium for displaying driving information. This makes the reading performance and user research of LCD vehicle dashboards critically important. In this study, we aim to evaluate the impact of the human-machine interface (HMI) design of vehicle LCD dashboards on driver readability and user experience. Twelve experts participated in a clustering experiment of vehicle dashboards, and 32 drivers participated in a simulated driving environment test of resident display information and temporary display information. The results for resident display information indicate that design type makes a significant difference in both reading performance and visual search efficiency. In addition, only the unconventional-shaped (L-shaped) dual-dial design achieves the ‘‘desired’’ rating in the user experience evaluations. Regarding temporary display information, findings indicate that icons positioned at the top of the dashboard interface are more readable than those at the bottom. Highprioritized or frequently used icons should be placed at the top of the screen. Alternatively, if icons must be positioned at the bottom, they should be no smaller than 8 mm in size. This research will help reduce the driver’s visual workload and off-road sight time, reduce the risk of traffic accidents, and improve the driver’s user experience.

2 versions available

Driving Simulation Performance and Fixational Eye Movement Under Different Photopic and Mesopic Luminance Intensities

Year: 2025

Authors: A Ahmad, SA Rosli, ASSM Zaini , International Conference on Man-Machine Systems (ICoMMS 2025)

The man-machine system is highly involved in driving simulation, as the simulation delivers visual and auditory feedback while drivers' actions, such as braking, become the input. Driving also requires proper visual attention, even under dimmed light intensity. This study compared the driving simulation performance and fixational eye movement under different luminance intensities of tinted lenses and with no tinted lenses. Thirty young adults between the ages of 20 to 28 years participated in this study. All subjects had good vision, proper driving experience, and a license. They were required to complete driving simulation tasks while wearing a headmounted eye tracker under a photopic baseline with no tinted lens, and different tinted lenses of 25%, 50%, and 75% luminance intensity, respectively. Driving simulation performance was evaluated based on course completion duration and braking time. Dikablis eye tracker was used to measure fixational eye movement, which consisted of duration and the number of fixations. The driving simulation performance through course completion duration and braking time differed significantly between different tinted lenses and baseline no lens [F(2.43,70.49)=15.03,p<0.001 and F(2.29, 66.32) = 3.98, p=0.02, respectively]. However, there was no significant difference in the duration and number of fixations between different tinted lenses and baseline no lens [F(2.77, 80.45)=0.56,p=0.63]. This study implied that low light transmission of luminance intensity, especially under mesopic light transmission, affected the drivers' actions in their driving performance, but not their fixational eye movement ability.

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