Behavioral research publication hub - Latest studies, white papers

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.

Simulator

Software

2 versions available

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.

Eye Tracking Glasses

Software

2 versions available

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.

Eye Tracking Glasses

Simulator

6 versions available

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.

Eye Tracking Glasses

Simulator

5 versions available

Exploring the occupational fatigue risk of short-haul truck drivers: effects of sleep pattern, driving task, and time-on-task on driving behavior and eye-motion metrics

Driver fatigue is the leading cause of truck-related accidents. The most significant occupational fatigue factors among short-haul truck drivers are sleep patterns, the round-trip driving task, and the time-on-task. However, the underlying mechanisms of these influential factors remain unclear. This study aims to explore the interactive effects of sleep patterns, driving task, and time-on-task on driving behavior and eye-motion metrics among short-haul truck drivers. We obtained test data from eleven professional short-haul truck drivers, with each driver participating in a three-day test under the conditions of two driving tasks and three different sleep patterns. We applied three-way repeated-measure ANOVA and non-parametric tests to analyze the data. The results reveal that: (1) violation of sleep-related legal requirements, insufficient sleep, and unreasonable time-on-task can have negative effects on short-haul truck drivers' vigilance and driving performance; (2) both driving task and sleep pattern contribute to driver fatigue, and the interaction of time-on-task and sleep pattern exacerbates driver fatigue more than the effects of any single factor alone; and (3) short-haul truck drivers who are sleep deprived exhibit short periods of controlled compensatory behavior during the outbound task, and sleepiness is more prevalent during the inbound task compared to the outbound task due to the monotony and low workload of the driving process. These findings provide theoretical and practical guidance for transportation industry managers to strengthen company-wide fatigue-related regulations, ensure adequate sleep for drivers via regulations, and optimize work schedules to improve safety outcomes of short-haul truck drivers.

Eye Tracking Glasses

Simulator

5 versions available

Gaze alternation predicts inclusive next-speaker selection: evidence from eyetracking

Next-speaker selection refers to the practices conversationalists rely on to designate who should speak next. Speakers have various methods available to them to select a next speaker. Certain actions, however, systematically co-select more than one particular participant to respond. These actions include asking “open-floor” questions, which are addressed to more than one recipient and that more than one recipient are eligible to answer. Here, next-speaker selection is inclusive. How are these questions multimodally designed? How does their multimodal design differ from the design of “closed-floor” questions, in which just one participant is selected as next speaker and where next-speaker selection is exclusive? Based on eyetracking data collected in naturalistic conversation, this study demonstrates that unlike closed-floor questions, open-floor questions can be predicted based on the speaker’s gaze alternation during the question. The discussion highlights cases of gaze alternation in open-floor questions and exhaustively explores deviant cases in closed-floor questions. It also addresses the functional relation of gaze alternation and gaze selection, arguing that the two selection techniques may collide, creating disorderly turntaking due to a fundamental change in participation framework from focally dyadic to inclusive. Data are in British and American English.

Eye Tracking Glasses

Software

1 version available:

GazeAway: Designing for Gaze Aversion Experiences

Gaze aversion is embedded in our behaviour: we look at a blank area to support remembering and creative thinking, and as a social cue that we are thinking. We hypothesise that a person's gaze aversion experience can be mediated through technology, in turn supporting embodied cognition. In this design exploration we present six ideas for interactive technologies that mediate the gaze aversion experience. One of these ideas we developed into “GazeAway”: a prototype that swings a screen into the wearer's field of vision when they perform gaze aversion. Six participants experienced the prototype and based on their interviews, we found that GazeAway changed their gaze aversion experience threefold: increased awareness of gaze aversion behaviour, novel cross-modal perception of gaze aversion behaviour, and changing gaze aversion behaviour to suit social interaction. We hope that ultimately, our design exploration offers a starting point for the design of gaze aversion experiences.

Eye Tracking Glasses

Software

3 versions available

GazeTrak: Exploring Acoustic-based Eye Tracking on a Glass Frame

In this paper, we present GazeTrak, the first acoustic-based eye tracking system on glasses. Our system only needs one speaker and four microphones attached to each side of the glasses. These acoustic sensors capture the formations of the eyeballs and the surrounding areas by emitting encoded inaudible sound towards eyeballs and receiving the reflected signals. These reflected signals are further processed to calculate the echo profiles, which are fed to a customized deep learning pipeline to continuously infer the gaze position. In a user study with 20 participants, GazeTrak achieves an accuracy of 3.6° within the same remounting session and 4.9° across different sessions with a refreshing rate of 83.3 Hz and a power signature of 287.9 mW. Furthermore, we report the performance of our gaze tracking system fully implemented on an MCU with a low-power CNN accelerator (MAX78002). In this configuration, the system runs at up to 83.3 Hz and has a total power signature of 95.4 mW with a 30 Hz FPS.

Eye Tracking Glasses

Software

3 versions available

Group Cycling in Urban Environments: Analyzing Visual Attention, Hazard Perception, and Riding Performance for Enhanced Road Safety

China is a major cycling nation with nearly 400 million bicycles. The widespread use of bicycles effectively alleviates urban traffic congestion. However, safety concerns are prominent, with approximately 35% of cyclists forming groups with family, friends, or colleagues, exerting a significant impact on the traffic system. This study focuses on group cycling, employing urban cycling experiments, GPS trajectory tracking, and eye-tracking to analyze the visual search, hazard perception, and cycling control of both groups and individuals. Findings reveal interdependence in visual attention among group cyclists in busy and complex road conditions, leading to reduced attention to traffic safety targets and potential decreases in risk perception. In terms of lateral control, group cycling exhibits lower lateral deviation and higher steering entropy, particularly at complex intersections. While group cycling results in decreased speed, it forms a clustering advantage at complex intersections, competitively advancing to shorten intersection passage times. Overall, group cyclists differ from individuals in visual, hazard perception, and control aspects, potentially elevating cycling risks. Consequently, there is a need for corresponding traffic safety education and intervention, along with consideration of group cycling characteristics in urban traffic planning to enhance safety and efficiency.

Eye Tracking Glasses

Simulator

1 version available:

Guiding gaze gestures on smartwatches: Introducing fireworks

Smartwatches enable interaction anytime and anywhere, with both digital and augmented physical objects. However, situations with busy hands can prevent user inputs. To address this limitation, we propose Fireworks, an innovative hands-free alternative that empowers smartwatch users to trigger commands effortlessly through intuitive gaze gestures by providing post-activation guidance. Fireworks allows command activation by guiding users to follow targets moving from the screen center to the edge, mimicking real life fireworks. We present the experimental design and evaluation of two Fireworks instances. The first design employs temporal parallelization, displaying few dynamic targets during microinteractions (e.g., snoozing a notification while cooking). The second design sequentially displays targets to support more commands (e.g., 20 commands), ideal for various scenarios other than microinteractions (e.g., turn on lights in a smart home). Results show that Fireworks’ single straight gestures enable faster and more accurate command selection compared to state-of-the-art baselines, namely Orbits and Stroke. Additionally, participants expressed a clear preference for Fireworks’ original visual guidance.

Eye Tracking Glasses

Software

4 versions available

Company

Publication Hub

Filter menu

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

Exploring the occupational fatigue risk of short-haul truck drivers: effects of sleep pattern, driving task, and time-on-task on driving behavior and eye-motion metrics

Gaze alternation predicts inclusive next-speaker selection: evidence from eyetracking

GazeAway: Designing for Gaze Aversion Experiences

GazeTrak: Exploring Acoustic-based Eye Tracking on a Glass Frame

Group Cycling in Urban Environments: Analyzing Visual Attention, Hazard Perception, and Riding Performance for Enhanced Road Safety

Guiding gaze gestures on smartwatches: Introducing fireworks

Explore Cutting-Edge Research in Human Factors and Ergonomics