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An exploration of drivers’ lane position after adding buffered cycling lanes in Guelph, Ontario

Dedicated cycling infrastructure, such as a buffered cycling lane, is implemented more frequently with the goal of improving cyclist safety by decreasing cyclist-vehicle interactions. While previous research has focused on evaluating driver lane position through passing events (when drivers overtake slower cyclists), little research has evaluated how drivers interact with novel cycling infrastructure in the absence of cyclists. Through an analysis of instrumented vehicle data from an on-road study in Guelph, Ontario, this study compares driver behaviors before-and-after modifying an existing cycling lane into a cycling lane with a painted buffer. It was found that drivers were significantly further from the marking of the cycling lane by an average of 31.6 cm when there was a traditional painted cycling lane, as opposed to a buffered cycling lane. This difference was greater than the change in vehicle lane width (narrowed on average by 22.7 cm). However, this may not change overall distance from cyclists when accounting for additional space from the buffer. Drivers did not differ in the standard deviation of their lane position, or in their speeds, between the two types of cycling lane. Findings from this research have implications for decisions regarding infrastructure and the development of automated driving systems.

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Analysis and regulation of driving behavior in the entrance zone of freeway tunnels: Implementation of visual guidance systems in China

In China, visual guidance systems are commonly used in tunnels to optimize the visual reference system. However, studies focusing specifically on visual guidance systems in the tunnel entrance zone are limited. Hence, a driving simulation test is performed in this study to quantitatively evaluate the effectiveness of (i) visual guidance devices at different vertical positions (pavement and roadside) and (ii) a multilayer visual guidance system for regulating driving behavior in the tunnel entrance zone. Furthermore, the characteristics of driving behavior and their effects on traffic safety in the tunnel entrance zone are examined. Data such as the vehicle position, area of interest (AOI), throttle position, steering wheel angle, and lane center offset are obtained using a driving simulation platform and an eye-tracking device. As indicators, the first fixation position (FP), starting deceleration position (DP), average throttle position (TPav), number of deceleration stages (N|DS), gradual change degree of the vehicle trajectory (G|VT), and average steering wheel angle (SWAav) are derived. The regulatory effect of visual guidance devices on driving performance is investigated. First, high-position roadside visual guidance devices effectively reduce decision urgency and significantly enhance deceleration and lane-keeping performance. Specifically, the advanced deceleration performance (AD), smooth deceleration performance (SD), trajectory gradualness (TG), and trajectory stability (TS) in the tunnel entrance zone improve by 63%, 225%, 269%, and 244%, respectively. Additionally, the roadside low-position visual guidance devices primarily target the trajectory gradualness (TG), thus resulting in improvements by 80% and 448% in the TG and TS, respectively. Meanwhile, the pavement visual guidance devices focus solely on enhancing the TS and demonstrates a relatively lower improvement rate of 99%. Finally, the synergistic effect of these visual guidance devices facilitates the multilayer visual guidance system in enhancing the deceleration and lane-keeping performance. This aids drivers in early detection and deceleration at the tunnel entrance zone, reduces the urgency of deceleration decisions, promotes smoother deceleration, and improves the gradualness and stability of trajectories.

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Association between length of upstream tunnels and visual load in connection zones of highway tunnel groups

To investigate drivers' visual load and comfort in the distance between adjacent tunnels (tunnel group connection zones), the maximum transient vibration value (MTVV) of the pupil area is used in this study as the index to analyze the visual load characteristics of the driver throughout the connection zones in highway tunnel groups. Data was collected using field driving experiments during which the pupil area change rate is measured as an additional indicator to evaluate the sufficiency of the length of the connection zones from the perspective of drivers’ visual adaptation. The findings show that the length of the upstream tunnel affects the visual strain of the drivers when they enter the connection zone. The visual load and its association with the length of the upstream tunnel appeared to be in the following descending order: short > extra-long > long > medium tunnel. The visual discomfort level in the short upstream tunnel has shown to be “uncomfortable,” while the level of comfort slightly rises to “fairly uncomfortable,” in the connection zone when the upstream tunnel is extra long and long. Departing from medium upstream tunnel resulted in the highest level of comfort “a little uncomfortable level” in the connection zone. When the upstream tunnels are short and medium in length, the required time for light adaptation is 5 s. The connection zone length threshold which is the minimum length of connection zone in order for two consecutive tunnels not to affect each other in terms of visual load of drivers is calculated to be 713.89 m. The driver's pupil area change during light adaptation when the upstream tunnel is short and medium is in the range of 30–40 %. When upstream tunnel is long and extremely long, the light adaptation time is 8 s and 9 s, respectively, and the respective thresholds for connection zone are 797.22 m and 825 m. The drivers' pupil area change in long and extremely long tunnels during light adaptation is in the range of 38–50 % and 43–50 %, respectively. Findings in this study can be used for the design of connection zones between tunnels in a highway tunnel group.

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Biosignals Monitoring for Driver Drowsiness Detection using Deep Neural Networks

Drowsy driving poses a significant risk to road safety, necessitating the development of reliable drowsiness detection systems. In particular, the advancement of Artificial Intelligence based neuroadaptive systems is imperative to effectively mitigate this risk. Towards reaching this goal, the present research focuses on investigating the efficacy of physiological indicators, including heart rate variability (HRV), percentage of eyelid closure over the pupil over time (PERCLOS), blink rate, blink percentage, and electrodermal activity (EDA) signals, in predicting driver drowsiness. The study was conducted with a cohort of 30 participants in controlled simulated driving scenarios, with half driving in a non-monotonous environment and the other half in a monotonous environment. Three deep learning algorithms were employed: sequential neural network (SNN) for HRV, 1D-convolutional neural network (1D-CNN) for EDA, and convolutional recurrent neural network (CRNN) for eye tracking. The HRV-Based Model and EDA-Based Model exhibited strong performance in drowsiness classification, with the HRV model achieving precision, recall, and F1-score of 98.28%, 98%, and 98%, respectively, and the EDA model achieving 96.32%, 96%, and 96% for the same metrics. The confusion matrix further illustrates the model's performance and highlights high accuracy in both HRV and EDA models, affirming their efficiency in detecting driver drowsiness. However, the Eye-Based Model faced difficulties in identifying drowsiness instances, potentially attributable to dataset imbalances and underrepresentation of specific fatigue states. Despite the challenges, this work significantly contributes to ongoing efforts to improve road safety by laying the foundation for effective real-time neuro-adaptive systems for drowsiness detection and mitigation.

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Breaking the silence: understanding teachers’ use of silence in classrooms

Silence in classrooms is an undervalued and understudied phenomenon. There is limited research on how teachers behave and think during teachers’ silence in lessons. There are also methodological constraints due to the lack of teacher’s talk during silence. This study used eye-tracking technology to visualize the noticing patterns of two science teachers during silence lasting more than three seconds. Using video data recorded from cameras and eye trackers, we examined each silent event and interpreted teachers’ perceptions and interpretations with consideration of eye fixations, actions of students and teachers during the silence, and teachers’ actions immediately after they broke the silence. We further examined expert-novice differences in teachers’ use of silence. Four categories of teachers’ silence were identified: silence for (1) preparing the classroom for learning; (2) teaching, questioning, and facilitating learning; (3) reflecting and thinking, and (4) behavioural management. Expert-novice differences were identified, especially in the teachers’ use of silence for approaches to teaching, reflection, and behavioural management. The novel contribution of this paper lies in the characterization of silences as observed in actual classroom settings as well as the methodological innovation in using eye trackers and video to overcome the constraints of lack of talk data during silence.

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Comparing eye–hand coordination between controller-mediated virtual reality, and a real-world object interaction task

Virtual reality (VR) technology has advanced significantly in recent years, with many potential applications. However, it is unclear how well VR simulations mimic real-world experiences, particularly in terms of eye–hand coordination. This study compares eye–hand coordination from a previously validated real-world object interaction task to the same task re-created in controller-mediated VR. We recorded eye and body movements and segmented participants’ gaze data using the movement data. In the real-world condition, participants wore a head-mounted eye tracker and motion capture markers and moved a pasta box into and out of a set of shelves. In the VR condition, participants wore a VR headset and moved a virtual box using handheld controllers. Unsurprisingly, VR participants took longer to complete the task. Before picking up or dropping off the box, participants in the real world visually fixated the box about half a second before their hand arrived at the area of action. This 500-ms minimum fixation time before the hand arrived was preserved in VR. Real-world participants disengaged their eyes from the box almost immediately after their hand initiated or terminated the interaction, but VR participants stayed fixated on the box for much longer after it was picked up or dropped off. We speculate that the limited haptic feedback during object interactions in VR forces users to maintain visual fixation on objects longer than in the real world, altering eye–hand coordination. These findings suggest that current VR technology does not replicate real-world experience in terms of eye–hand coordination.

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Designing an Experimental Platform to Assess Ergonomic Factors and Distraction Index in Law Enforcement Vehicles during Mission-Based Routes

Mission-based routes for various occupations play a crucial role in occupational driver safety, with accident causes varying according to specific mission requirements. This study focuses on the development of a system to address driver distraction among law enforcement officers by optimizing the Driver–Vehicle Interface (DVI). Poorly designed DVIs in law enforcement vehicles, often fitted with aftermarket police equipment, can lead to perceptual-motor problems such as obstructed vision, difficulty reaching controls, and operational errors, resulting in driver distraction. To mitigate these issues, we developed a driving simulation platform specifically for law enforcement vehicles. The development process involved the selection and placement of sensors to monitor driver behavior and interaction with equipment. Key criteria for sensor selection included accuracy, reliability, and the ability to integrate seamlessly with existing vehicle systems. Sensor positions were strategically located based on previous ergonomic studies and digital human modeling to ensure comprehensive monitoring without obstructing the driver’s field of view or access to controls. Our system incorporates sensors positioned on the dashboard, steering wheel, and critical control interfaces, providing real-time data on driver interactions with the vehicle equipment. A supervised machine learning-based prediction model was devised to evaluate the driver’s level of distraction. The configured placement and integration of sensors should be further studied to ensure the updated DVI reduces driver distraction and supports safer mission-based driving operations.

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Dynamic Alert Design Based on Driver’s Cognitive State for Take-over Request in Automated Vehicles

This thesis investigates the effectiveness of dynamic alert systems tailored to drivers’ cognitive states in automated driving environments, focusing on enhancing takeover readiness during critical transitions. Utilizing a large-scale immersive driving simulation, the study evaluated drivers’ response times and physiological measures when reacting to various alert intensities and the presence of a secondary typing task. The experiment revealed that dynamic alerts significantly improved response times and takeover performance, especially in high-distraction scenarios. Drivers responded more effectively when alerts were adjusted to their cognitive load, with strong alerts resulting in the fastest reaction times under distracted conditions. On average, dynamic alerts reduced response times by approximately 1.75 seconds compared to static alerts. Additionally, higher lateral accelerations were observed under strong alerts, indicating more decisive maneuvering. Self-rated attention-capturing scores were notably higher with dynamic alerts, particularly under strong alert conditions and in the presence of secondary tasks. The ANOVA results showed significant improvements in attention capturing and overall alert effectiveness when dynamic alerts were employed, demonstrating the robust design’s ability to capture attention and enhance driver responsiveness. The study confirmed that adaptive alert designs, which adjust based on the driver’s cognitive state, can markedly enhance overall driving experience and safety. Participants reported higher levels of confidence with dynamic alerts, especially in scenarios involving secondary tasks. Despite the strong alerts, annoyance levels remained low, indicating that dynamic alerts are effective without causing undue stress. These results underscore the potential of using adaptive systems to improve safety and efficiency in automated driving, advocating for a more nuanced approach to system alerts that considers the variable cognitive states of drivers. Future research should validate these findings with on-road studies, explore a broader range of alert modalities, and refine physiological monitoring techniques to further enhance adaptive alert systems.

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Dynamic driving risk in highway tunnel groups based on pupillary oscillations

This study aims to understand the dynamic changes in driving risks in highway tunnel groups. Real-world driving experiments were conducted, collecting pupil area data to measure pupil size oscillations using the Percentage of Pupil Area Variable (PPAV) metric. The analysis focused on investigating relative pupil size fluctuations to explore trends in driving risk fluctuations within tunnel groups. The objective was to identify accident-prone areas and key factors influencing driving risks, providing insights for safety improvements. The findings revealed an overall “whipping effect” phenomenon in driving risk changes within tunnel groups. Differences were observed between interior tunnel areas and open sections, including adjacent, approach, and departure zones. Higher driving risks were associated with locations closer to the tail end of the tunnel group and shorter exit departure sections. Targeted safety improvement designs should consider fluctuation patterns in different directions, with attention to tunnels at the tail end. In open sections, increased travel distance and lengths of upstream and downstream tunnels raised driving risks, while longer open zones improved driving risks. Driving direction and sequence had minimal impact on risks. By integrating driver vision, tunnel characteristics, and the environment, this study identified high-risk areas and critical factors, providing guidance for monitoring and improving driving risks in tunnel groups. The findings have practical implications for the operation and safety management of tunnel groups.

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Evaluation of driver’s situation awareness in freeway exit using backpropagation neural network

Based on combining the relevant studies on situation awareness (SA), this paper integrated multiple indicators, including eye movement, electroencephalogram (EEG), and driving behavior, to evaluate SA. SA is typically divided into three stages: perception, understanding, and prediction. This paper used eye movement indicators to represent perception, EEG indicators to represent understanding, and driving behavior indicators to represent prediction. After identifying indicators for evaluating SA, a driving simulation experiment was designed to collect data on the indicators. 41 subjects were recruited to participate in the investigation, and the experimenter collected data from each subject in a total of 9 groups. After removing 4 groups of invalid data, 365 groups of valid data were finally obtained. The grey correlation analysis was used to optimize the SA indicators, and 10 SA evaluation indicators were finally determined. There were the average fixation duration, the nearest neighbor index, pupil area, the percentage power spectral density values of the 3 rhythmic waves (θ, α, β), rhythmic wave energy combination parameters (α/θ), mean speed, SD of speed and acceleration. Taking the optimized 10 indicators as input and the SA scores as output, a backpropagation neural network model with a topological structure of 10-8-1 was constructed. 75% of the data were randomly selected for model training, and the final network training’s mean square error was 0.0025. Using the remaining 25% of data for verification, the average absolute error and average relative error of the predicted results are 0.248 and 0.046, respectively. This showed that the model was effective, and it was feasible to evaluate the SA by using the data of eye movement, EEG and driving behavior parameters.

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An exploration of drivers’ lane position after adding buffered cycling lanes in Guelph, Ontario

Analysis and regulation of driving behavior in the entrance zone of freeway tunnels: Implementation of visual guidance systems in China

Association between length of upstream tunnels and visual load in connection zones of highway tunnel groups

Biosignals Monitoring for Driver Drowsiness Detection using Deep Neural Networks

Breaking the silence: understanding teachers’ use of silence in classrooms

Comparing eye–hand coordination between controller-mediated virtual reality, and a real-world object interaction task

Designing an Experimental Platform to Assess Ergonomic Factors and Distraction Index in Law Enforcement Vehicles during Mission-Based Routes

Dynamic Alert Design Based on Driver’s Cognitive State for Take-over Request in Automated Vehicles

Dynamic driving risk in highway tunnel groups based on pupillary oscillations

Evaluation of driver’s situation awareness in freeway exit using backpropagation neural network

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