Driver Eye Movements Research Articles

Do dotted extensions help guide drivers at a transition area of partial-shadowed left-turn lane? An investigation based on eye movement behavior and visual attention

When adding an exclusive left-turn lane at an intersection, a transition area is needed to allow through and left-turn traffic to move into appropriate lanes smoothly. However, whether a dotted extension should be applied at such a transition area remains inconclusive. The need for recommending a dotted extension is further complicated at intersections where an approach taper partially shadows a left-turn lane. This study aims to shed light on whether dotted extensions should be recommended in a transition area and research the impacts of dotted extensions on guiding drivers from their perspectives. Scenarios with and without dotted extensions were created for a video-viewing experiment. Driver eye movement behavior and cognitive load rating served as evaluation indicators in each target scenario. Thirty participants were recruited and randomly assigned to with and without dotted extension groups. Results show that: (1) With the dotted extension, participants could focus on fewer objects and, therefore, would not need to divert their visual attention toward the surrounding vehicles. (2) The dotted extension assists through-traffic drivers in maintaining more focused attention on information critical to their driving task. (3) Dotted extensions help drivers reduce stress, mainly because of clearer indications of lane configuration. Altogether, our findings suggest that a road with dotted extensions provides better guidance in a transition area than one without dotted extensions.

Impact of Median Separation Facilities on Drivers’ Shy-Away Effects: Why Are Median Separation Facilities Widely Used in China?

In recent years, China has begun introducing readily installable median separation facilities into its urban road network. Although some studies have recognized the benefits of using median separation, few have investigated the effects of different types of facilities on driver behavior. This driving simulator study aims to determine whether different types of medians on urban arterial roads in China affect driving behavior, and to provide new insights for designing more effective and safer forms of median separation. A driving simulator study was conducted, with 50 participants. The scenario exactly reproduced an existing six-lane urban arterial road in China, and four different median separation types were tested: (1) double yellow line; (2) raised pavement markers; (3) reboundable traffic cylinders; (4) traffic separator railing. Their effects were statistically analyzed using data on driver eye movements, trajectories, and speeds collected from simulation tests. The results showed that the median separation facilities significantly improved drivers’ access to road information, corrected drivers’ attention, and suppressed driving speeds. Although median separation facilities can be an effective solution to the current problem of chaotic vehicular traffic on China’s urban roads, their setup still needs to be optimized. This study demonstrates the calculation method for optimal median facility setup. The resulting 1.21-m-high median facility, paired with a 1.17-m-wide median strip and a certain lane-width compression, more effectively addresses driver factors, the driving task, and the spatial right-of-way. The study’s calculations can also be used to improve other types of roads that can take a median separation facility.

Why frequent traffic accidents at highway tunnel exit? – An experimental analysis of the slack effect

This study analyzes the causes of frequent traffic accidents at the exit areas of highway tunnels. Vehicle experiments were conducted to collect data on the light environment, vehicle speed, and driver eye movement. The distribution patterns of the illumination, vehicle speed, and gaze location occupancy were analyzed, and highway tunnels of different lengths were compared. The tunnel exit area is divided into the transition, exit, and departure zones. It was observed that there was an obvious white-hole effect at the exit zone, with drastic changes in the light environment. The presence of abnormal acceleration behavior in the transition zone and decreased attention in the departure zone demonstrate the existence of psychological and physiological slack in drivers. Additionally, the tunnel length positively correlates to the slack effect, which is stronger in extra-long tunnels than in the short and medium tunnels. Multiple control scenarios were established and driving simulation experiments were conducted to further analyze the impact of the slack effect on the traffic safety. The experimental results demonstrated that the slack effect of the extra-long tunnels exhibited an evident lag which was still present after leaving the tunnel exit. The average vehicle speed and trajectory offset were larger and more discrete in the post-tunnel zone than on an ordinary open road. The speed of the curve in the post-tunnel zone is 5.1 % higher than that of the curve in the ordinary road, and the maximum trajectory offset is 54.3 % higher. Thus, this study reveals the slack effect at tunnel exits and explains the abnormal phenomenon of relatively good traffic environments but high traffic accident rates in the transition and departure zones of tunnel exits. These results present considerable practical significance for the safe operation of highway tunnels, particularly extra-long tunnels.

Detection of distracted driving through the analysis of real-time driver, vehicle, and roadway volatilities

Distracted driving adversely impacts drivers’ decision-making and leads to safety-critical events (SCEs). Early detection of driver distraction is critical to prevent traffic crashes by providing warning messages to drivers and the surrounding vehicles. This study harnesses real-time multidimensional data collected through sensors that examine the variations in driver biometrics, vehicle kinematics, and roadway surroundings in different driving scenarios conducted on a Multimodal Virtual Reality Simulator. The driving behaviors of the study participants were examined under various visual detection response tasks of increasing complexity. The study classifies driving behaviors on a 5-level ordinal scale by estimating a Panel Ordered Logit Model, Random Forest, and Artificial Neural Network, using real-time volatilities in driver biometric signals, vehicle speed and acceleration, and roadway surroundings. The study results reveal that the driver gaze and the coefficients of variation in vehicle speed, driver eye movements, vehicular distances from the lane centerline, and the following vehicle significantly impact distracted driving. The study’s findings align with the principles of the safe systems approach by emphasizing the development of proactive safety measures in the form of feedback and warning the driver and surrounding vehicles of a potential distracted driving event, helping to foster safer user behavior and vehicles.

Impact of Road Central Greening Configuration on Driver Eye Movements: A Study Based on Real Vehicle Experiments

Safe driving depends on drivers’ ability to rapidly and accurately process information in varying traffic conditions. The presence of central green landscapes on roads is a key factor in this context. However, there is a gap in current research, which tends to focus on qualitative aspects of landscape design while lacking quantitative data-driven analyses. In this study, we aim to address this gap by investigating the impact of road central greening configuration on the eye movements of young novice drivers, a population particularly sensitive to external environmental changes. Specifically, we explore the influence of central green landscapes on four visual parameters: driver gaze, saccade, blinking, and pupil behavior. Through real vehicle experiments conducted on different road sections, we collected visual feature data to comprehensively analyze the patterns of driver eye movements. Our findings reveal that the introduction of central green landscapes can exert cognitive pressure on drivers, leading to specific alterations in their visual parameters. These changes include dispersed gaze points, reduced effective gaze durations, increased gaze frequencies, extended saccade durations and angles, heightened blink durations and frequencies, and reduced pupil area. By shedding light on the intricate interplay between road central greenery and driver behavior, this study provides valuable insights for optimizing landscape design in transportation planning and enhancing road safety considerations.

Real Time Driver Gaze Tracking and Eyes off the Road Detection System

Abstract: Driver Gaze Tracking and Eyes Off the Road Detection System is a technology-driven solution designed to enhance road safety by monitoring driver behavior and detecting instances when the driver's attention is diverted from the road. The system utilizes computer vision techniques and machine learning algorithms to track the driver's eye movements and identify potential distractions. By providing real-time alerts and warnings, this system aims to mitigate the risk of accidents caused by distracted driving. Distracted driving poses a significant risk to road safety, and traditional methods have limitations in ensuring immediate compliance. By leveraging computer vision and machine learning, the system provides a proactive approach to detect distractions and alert drivers, ultimately reducing the occurrence of accidents caused by diverted attention. In conclusion, the Driver Gaze Tracking and Eyes Off the Road Detection System offers a technological solution to enhance road safety by monitoring driver behavior and detecting distractions. By promptly alerting drivers when their attention is diverted, this system contributes to mitigating the risks associated with distracted driving and promoting safer road environments.

An Automatic Driver’s Drowsiness Alert System

Abstract: The Automatic Driver's Drowsiness Alert System (ADDAS) using a PIC microcontroller is a technological solution that aims to prevent accidents caused by drowsy driving. This system uses a combination of sensors and algorithms to detect the driver's level of alertness and sends a warning when the driver is experiencing drowsiness or fatigue. The system is based on a PIC microcontroller that processes the data obtained from the sensors, which include an eyeblink sensor, a temperature sensor, a Heartbeat sensor, GSM (Global System for Mobile Communication) module and GPS (Global Positioning System). The eyeblink sensor monitors the driver's eye movements, the temperature sensor measures the driver's temperature, heartbeat sensor measures the pulse rate of driver, alcohol sensor measures alcohol intake of driver. The data is processed using an algorithm that analyses the driver's behaviour and physiological parameters to determine the level of alertness. If the driver is found to be drowsy or fatigued, the system sends a warning to alert the driver, such as an audible alarm. This system is efficient, reliable, and cost-effective, making it an ideal solution for preventing accidents caused by drowsy driving. It promotes safer roads and reduces the risk of fatalities, making it an essential tool for enhancing driver safety

Implementation of Driver Drowsiness Detection and Accident Prevention System Using Wireless Sensors

Nowadays we can see a lot of accidents occurring on the road. And nearly 78.4% of road accidents are occurring due to the drowsiness of drivers riding the vehicles. To avoid road accidents, there is a need for a driver assistance system for monitoring the activities of drivers. The proposed technique monitors the driver's eye movements using an eye blink sensor located in the device, in case of driver closes the eye frequently or other abnormal moments occurred, the eye blink sensors produce the alarm sound for altering the driver to stop the vehicle with an IR sensor attached to it. Also, an alcohol sensor is attached to it which detects the alcohol consumed by the driver and in case of alcohol is detected, it buzzes an alarm which in order avoids the road accidents.

Research on driving visual comfort of two‐lane mountainous road based on traffic safety evaluation

AbstractIn order to obtain the quantitative relationship between multisource data from road landscape and driving visual comfort in two‐lane mountainous road, we collected the driver's eye movement data and road environment data through real vehicle tests. The threshold segmentation method and the target landscape area ratio method are used to process the visual image, and the spatial enclosure degree is used to express the driver's sense of spatial closure. In addition, an HSV color model is established to characterize the driver's visual perception of the mountain highway landscape environment. Combining the target landscape area ratio of road landscape with the amount of color information, the calculation method of road landscape information is obtained. According to the characteristics of normal distribution of pupil area ratio, five grades of driver's visual comfort level are proposed, and the regression relationship between comfort level and landscape information is established. The study found that there is a significant negative correlation between the amount of road landscape information and the driver's visual comfort. When the amount of road landscape information is less than 0.338, the driving comfort is higher, which is conducive to driving safety. However, when the amount of landscape information in the road area exceeds 0.644, the driving comfort decreases significantly, which is detrimental to driving safety.

Stopping by looking: A driver-pedestrian interaction study in a coupled simulator using head-mounted displays with eye-tracking

Automated vehicles (AVs) can perform low-level control tasks but are not always capable of proper decision-making. This paper presents a concept of eye-based maneuver control for AV-pedestrian interaction. Previously, it was unknown whether the AV should conduct a stopping maneuver when the driver looks at the pedestrian or looks away from the pedestrian. A two-agent experiment was conducted using two head-mounted displays with integrated eye-tracking. Seventeen pairs of participants (pedestrian and driver) each interacted in a road crossing scenario. The pedestrians' task was to hold a button when they felt safe to cross the road, and the drivers' task was to direct their gaze according to instructions. Participants completed three 16-trial blocks: (1) Baseline, in which the AV was pre-programmed to yield or not yield, (2) Look to Yield (LTY), in which the AV yielded when the driver looked at the pedestrian, and (3) Look Away to Yield (LATY), in which the AV yielded when the driver did not look at the pedestrian. The driver's eye movements in the LTY and LATY conditions were visualized using a virtual light beam. Crossing performance was assessed based on whether the pedestrian held the button when the AV yielded and released the button when the AV did not yield. Furthermore, the pedestrians' and drivers' acceptance of the mappings was measured through a questionnaire. The results showed that the LTY and LATY mappings yielded better crossing performance than Baseline. Furthermore, the LTY condition was best accepted by drivers and pedestrians. Eye-tracking analyses indicated that the LTY and LATY mappings attracted the pedestrian's attention, while pedestrians still distributed their attention between the AV and a second vehicle approaching from the other direction. In conclusion, LTY control may be a promising means of AV control at intersections before full automation is technologically feasible.

The impact of different AR-HUD virtual warning interfaces on the takeover performance and visual characteristics of autonomous vehicles

Objective The objective of this study was to determine the different effects of the arrow-pointing augmented reality head-up display (AR-HUD) interface, virtual shadow AR-HUD interface, and non-AR-HUD interface on autonomous vehicle takeover efficiency and driver eye movement characteristics in different driving scenarios. Methods Thirty-six participants were selected to carry out a simulated driving experiment, and the eye movement index and takeover time were analyzed. Results The arrow pointing AR-HUD interface and the virtual shadow AR-HUD interface could effectively reduce the driver's visual distraction, improve the efficiency of obtaining visual information, reduce the number of times the driver's eyes leave the road, and improve the efficiency of the takeover compared with the non-AR-HUD interface, but there was no significant difference in eye movement indexes between the arrow pointing AR-HUD interface and the more eye-catching virtual shadow AR-HUD interface. When specific scenarios were considered, it was found that in the scenario of emergency braking of the vehicle in front, the arrow pointing AR-HUD interface and the virtual shadow AR-HUD interface had more advantages in takeover efficiency than the non-AR-HUD interface. However, in the scenarios of a rear vehicle overtaking the vehicle ahead and non-motor vehicles running red lights, there was no significant difference in takeover efficiency. For the non-motor vehicle invading the line, emergency U-turn of the vehicle in front, and pedestrian crossing scenarios, the virtual shadow AR-HUD interface had the highest takeover efficiency. Conclusions These research results can help improve the active safety of autonomous vehicle AR-HUD interfaces.

Computational modeling of driver pre-crash brake response, with and without off-road glances: Parameterization using real-world crashes and near-crashes

When faced with an imminent collision threat, human vehicle drivers respond with braking in a manner which is stereotypical, yet modulated in complex ways by many factors, including the specific traffic situation and past driver eye movements. A computational model capturing these phenomena would have high applied value, for example in virtual vehicle safety testing methods, but existing models are either simplistic or not sufficiently validated. This paper extends an existing quantitative driver model for initiation and modulation of pre-crash brake response, to handle off-road glance behavior. The resulting models are fitted to time-series data from real-world naturalistic rear-end crashes and near-crashes. A stringent parameterization and model selection procedure is presented, based on particle swarm optimization and maximum likelihood estimation. A major contribution of this paper is the resulting first-ever fit of a computational model of human braking to real near-crash and crash behavior data. The model selection results also permit novel conclusions regarding behavior and accident causation: Firstly, the results indicate that drivers have partial visual looming perception during off-road glances; that is, evidence for braking is collected, albeit at a slower pace, while the driver is looking away from the forward roadway. Secondly, the results suggest that an important causation factor in crashes without off-road glances may be a reduced responsiveness to visual looming, possibly associated with cognitive driver state (e.g., drowsiness or erroneous driver expectations). It is also demonstrated that a model parameterized on less-critical data, such as near-crashes, may also accurately reproduce driver behavior in highly critical situations, such as crashes.

An image processing-based system proposal for real-time detection of drowsiness from a vehicle driver's eye movements

Drowsiness is one of the underlying causes of driving accidents leading to serious injuries and deaths annually. According to the experts has mentioned that almost 30% of all traffic accidents have been caused by drowsiness. In avoiding these traffic accidents, a proper system is required to prevent the driver from falling asleep. This study proposes a real-time image processing-based system for recognizing the drowsiness face expression of the vehicle driver. The method of this study, detecting the exact position facial landmarks and both left and right eyes using dlib and eye aspect ratio algorithm. This system, after detecting drowsiness eye, give audible alert the vehicle driver to stay awake throughout the driving journey.

How pipefitters obtain visual information from construction assembly drawings

An extensive framework has been developed for studying the behavior of motor vehicle drivers using eye tracking technology. Previous work has revealed strong relationships between driver eye movements and performance, which has resulted in widely accepted guidance within the transportation industry. In this work, the same eye tracking analysis methods were applied to investigate 20 professional pipefitters’ interactions with traditional isometric assembly drawings during a construction pipe model assembly task, in order to begin to understand the strategies that construction craft professionals use to gather visual information from engineering deliverables. A custom web application was developed to quantify and compare the pipefitters’ interactions with the assembly drawings through several visit metrics. Results indicated that the pipefitters’ interactions with the assembly drawings were associated with their performance and spatial cognition; however, the results did not suggest that the pipefitters were adhering to any particular visual information gathering strategies. The authors also investigated whether age or industry experience were associated with differences in visual information gathering strategies, but no significant relationships were observed. The primary contribution of this work is a demonstration of how existing eye tracking analysis methods can be applied to investigate how construction craft professionals extract visual information from engineering deliverables.

Study on the Law of Drivers’ eye Movement Index in Different Landscape Environment

To study the law of drivers’ eye movement indices,indoor simulation driving experiment method was adopted, two-lane second-class grassland highway was designed to simulate real road conditions. 48 subjects were selected for the experiment,groups were divided according to different driving ages, three eye movement sensitive indexes including fixation duration, pupil diameter and scan range were extracted, the variation rule of eye movement index of drivers with different experience under different landscape environment was quantified. It was found that the relationship between the landscape environments and eye movement index was U-shaped,the changes of eye movement indexes of drivers at different driving ages were different.The results showed that 4/km was the optimal threshold number of combined elements.In this environment, the change rate of driver eye movement index was the largest,extraction information was the most;When the number of elements was greater than or less than 4/km, the change rate of driver eye movement index would decrease. The results could provide reference for improving and optimizing the design of grassland highway landscape environment.

Method of Setting Exit Advance Guide Signs in Highway Tunnels Based on the Driver’s Eye Movement With Markov Chains

While the number of highway tunnels is increasing, the current Chinese criteria and Uniform Traffic Control Equipment Manual (MUTCD) 2009 guidelines provide no clear method for setting exit advance guide signs in highway tunnels. To solve this problem, this paper proposes separate guide signs, consisting of location and distance signs. Location signs on the tops of tunnels use Chinese characters with a height of 40 cm and a width 20 cm. The distance signs are 62.6 m from the location signs with a character height and width of 1.5 m and a total sign length of 22.5 m. Four schemes were tested, in which the distance sign was on the left or right wall of the tunnel and the vehicle was in the left or right lane. At the “Perception” and “Recognition” stages, the pupil area change rate of the four schemes had a slight difference to no sign but were always less than the 20% threshold line. A Markov chain was employed to analyze the driver's eye movement characteristics to evaluate the effect of separate guide signs. The results are the following. In the left lane, when a distance sign on the left wall of the tunnel appeared, the driver's fixation points were relatively balanced, except in the left area. After the sign disappeared, the driver's fixation points were no different than before, except in the front area. However, when a distance sign appeared on the right wall, 77.84% of the driver's fixation points were concentrated in the front and right areas, which decreased the driving safety. Similar results were obtained in the right lane. Therefore, distance signs should be placed on the left wall of the highway tunnel. This study can forewarn a driver in a tunnel of the exit location and distance and help the driver to exit the highway safely.

Safety Assessment of Exit Advance Guide Signs in Mountainous Highway Tunnel Based on Eye-Tracking Technology

Exit Advance Guide Signs play an important role in driving safety on highway tunnels. With the increase of mountain highway in recent 10 years, how to set exit advance guide signs inside the mountain highway tunnels become a new problem in China. This paper evaluates the effects of setting schemes of Exit Advance Guide Signs in mountain tunnels through simulation experiments. Specially, exit advance guide signs in the mountainous highway tunnel were divided into location sign and distance sign. Six schemes of different height, width and spacing were tested, in which the distance sign was on the left or right wall of the tunnel, and the vehicle was on the left lane. A Markov chain was used to analyze the driver's eye movement characteristics to evaluate the effect of separate guide signs. The results were as follows: if the distance sign was set on the left wall, the scheme 4 where height, width and spacing of 1.5m was the best choice, because the change rate of the driver's pupil area was higher than the threshold, attention to distance signs increased to 10.42%, and dashboard gaze also increased. These characteristics show that the traffic sign setting effect was obvious, and didn't affect the driver's driving safety; if the distance sign was set on the right wall, the scheme 6 where height, width and spacing of 2m was the best choice, because drivers pay the highest attention to area 5 and it did not exceed 60%, Meanwhile, the driver's fixation distribution of scheme 6 was the most stable. This study can optimize exit advance guide signs setting in the mountainous highway tunnel, then contributing to reduced traffic accidents and making mountain highway transport more sustainable.

Internal causes of return trip effect based on eye movement and EEG indices

When traveling by car, the driver normally feels that the return trip is shorter than the outbound trip. The reason for this feeling, called return trip effect, is not clear. To explore the internal causes of this return trip effect, an indoor driving simulation experiment and a real car driving experiment were performed in this study. Questionnaires were used to obtain the estimated time of the outbound and return trips of the drivers. An eye tracker and an electroencephalograph equipment were used to record driver's eye movement and EEG data in the real-car driving experiment. The physiological indices and questionnaire results showed that the difference in the driver's cognitive loads of the outbound and return trips was the internal cause of the return trip effect. Drivers who were unfamiliar with the road had a different cognitive load between the two trips, which resulted in the return trip effect. However, drivers who were familiar with the road did not experience a return trip effect due to the close cognitive loads of the two trips.

Driver eye movements in relation to unfamiliar traffic signs: An eye tracking study

Traffic signs are an integral part of the traffic control plan and they provide road users with necessary information on the upcoming situation. This paper aims to examine the level of understanding of traffic sign imagery used in different countries and to track participants' eye movement when they encounter unfamiliar signs. Tobii eye tracking glasses were used to track gaze differences between familiar and unfamiliar traffic signs. Our findings show that sign characteristics (such as the amount of information on the sign) and the observer's knowledge of the sign meaning have a significant impact on eye behaviour. Signs containing more information (loaded with more content) and unfamiliar to the participant systematically produced the longest overall and average fixations and gazing duration. Given that longer gaze time for unfamiliar traffic signs presents a potential traffic hazard, we evaluated the need for standardization of traffic signs.

Modeling of Driver eye movement that reflects attentional state

Modeling of Driver eye movement that reflects attentional state