The Role of Eye Tracking Technology in Assessing Older Driver Safety

Pilot Study on Gaze Characteristics of Older Drivers While Watching Driving Movies

Objective: This study aims to clarify the gazing characteristics of older drivers while driving cars using a gaze analysis device. Methods: The participants included 16 older and 12 middle-aged drivers who drove cars daily. After conducting cognitive and attentional function tests, eye gaze while watching driving videos was measured using an eye tracker. Ten driving videos were prepared. In addition, a total of 34 hazard areas were analyzed. Results: The results of the gaze measurement parameters were statistically compared between the two groups. In the older group, the gaze analysis results indicated that while viewing driving videos, the search for areas close to the car was expanded. In addition, in several hazard areas, we observed a decrease in the number of drivers gazing at the driver, shortened total gazing time, delay in the timing of gazing, and decrease in the number of visits. Conclusions: Older drivers' eye movement is increased; however, it is characterized by gazing at unimportant areas, indicating an inefficient scanning pattern. Although these results do not indicate an obvious decline in driving ability among older drivers, the decline in hazard perception may become apparent in some situations. The data contain underpowered results and require revalidation in larger studies.

Different deployments of attentional breadth selectively predict UFOV task performance in older adults

The Useful Field of View task (UFOV) is a strong and reliable predictor of crash risk in older drivers. However, while the functional domain of attention is clearly implicated in UFOV performance, the potential role of one specific attentional process remains unclear: attentional breadth (the spatial extent of the attended region around the point of visual fixation). The goal of the present study was to systematically test the role of two distinct aspects of attentional breadth, maintaining a specific breadth of attention and resizing the attended region, in UFOV performance. To this end, 135 older adults completed the UFOV and modified Navon tasks to measure their efficiency in maintaining, contracting, and expanding the breadth of attention. We then examined individual-difference associations between these aspects of attentional breadth deployment and UFOV performance. We found that performance on UFOV Subtask 2 was associated with efficient contraction of attentional breadth (i.e., resizing the attended region to a smaller area), while Subtask 3 performance was associated with the efficiency of expanding attentional breadth (i.e., resizing the attended region to a larger area). The selectivity of these relationships appears to implicate these specific deployments of attentional breadth in how people complete the task, as it suggests that these relationships are not simply attributable to shared variance in a broader domain of cognitive functioning. The implications of these results for our understanding of UFOV, as well as future research directions that test the relative contributions of different cognitive processes in predicting task performance, are discussed.

Effects of mobile phone-related distraction on driving performance at roundabouts: Eye movements tracking perspective

Modern road infrastructures are complex networks featuring various elements such as roads, bridges, intersections, and roundabouts, with advanced control systems. Roundabouts have gained prominence as a safer alternative to traditional intersections promoting smoother traffic flow and fewer collisions by guiding traffic in one direction, encouraging reduced speed, and minimizing conflict points.This study investigated driver behavior within roundabouts, focusing on gaze behavior, particularly the left-side mirror and window, under mobile phone distraction conditions. In addition, the effects of roundabout specifications (i.e., number of lanes and size of the central island) and the drivers' characteristics (i.e., driving experience) were examined.In total, 43 participants, aged 19-56 years including 30 males and 13 females, held a valid driving license, drove through a virtual simulated urban road containing four roundabouts, implemented in a static driving simulator, under baseline condition (no distraction) as well as mobile-induced distraction. Driving simulator data were collected and drivers' gaze direction and fixation on nine areas of interest were captured with an eye tracker.

Results

showed that experienced drivers exhibit a more fixation on the left-side mirror and window and were less distracted. Moreover, the road environment, i.e., the number of cars and the roundabout size, significantly influenced the drivers' attention. As regards the driving performance, the number of infractions increased when the drivers diverted focus from the left side of the car. The outcomes of the present study might help to improve traffic safety at roundabouts.

Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis

As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of databases were searched for English- language articles published up until March 1, 2024, that examined the relationships between artificial intelligence techniques, eye movements, and Alzheimer's disease. A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep learning, and machine learning, is required to enhance Alzheimer's disease detection using eye movement data. One sort of deep learning technique that shows promise is convolutional neural networks, which need further data for precise classification. Nonetheless, machine learning models showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering the development of early and precise Alzheimer's disease diagnosis. A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties with early Alzheimer's disease detection, this presents a novel strategy that may have consequences for clinical evaluations and customized medication to improve early and accurate diagnosis.

Differences in gaze behaviors between trainees and experts during endovascular therapy for cerebral aneurysms: a preliminary study using a cerebral aneurysm model

In the neuroendovascular field, the training of operators has become an important issue. Recently, eye-tracking technology has been introduced into various fields of medical education. This study aimed to apply eye-tracking technology to the training of neuroendovascular therapy. Six neurosurgeons, including three neuroendovascular specialists and three trainees, at our institution and related facilities participated in the study. Eye movement was recorded by the eye-tracking device during the microcatheter navigation and coil placement into the silastic aneurysm model under biplane X-ray fluoroscopy. Eye-tracking analysis during neuroendovascular therapy was feasible in all six subjects. In microcatheter navigation, specialists tended to more frequently switch their attention between frontal and lateral images than trainees. In coil embolization, the overall gaze frequency tended to increase, and the average fixation duration tended to decrease as the number of experienced cases increased. Inexperienced operators tend to fix their gaze when they are operators than when they are assistants. More experienced operators tended to look at the microcatheter longer in the coil insertion task. The eye-tracking analysis may be useful for operator training in neuroendovascular therapy. Experts may have moved their eyes more frequently than trainees to gaze at the right place. In the future, it will be necessary to collect gaze data for more operators in various tasks.

The Impact of Physical Motion Cues on Driver Braking Performance: A Clinical Study Using Driving Simulator and Eye Tracker

Driving simulators are increasingly being incorporated by driving schools into a training process for a variety of vehicles. The motion platform is a major component integrated into simulators to enhance the sense of presence and fidelity of the driving simulator. However, less effort has been devoted to assessing the motion cues feedback on trainee performance in simulators. To address this gap, we thoroughly study the impact of motion cues on braking at a target point as an elementary behavior that reflects the overall driver's performance. In this paper, we use an eye-tracking device to evaluate driver behavior in addition to evaluating data from a driving simulator and considering participants' feedback. Furthermore, we compare the effect of different motion levels ("No motion", "Mild motion", and "Full motion") in two road scenarios: with and without the pre-braking warning signs with the speed feedback given by the speedometer. The results showed that a full level of motion cues had a positive effect on braking smoothness and gaze fixation on the track. In particular, the presence of full motion cues helped the participants to gradually decelerate from 5 to 0 ms^-1 in the last 240 m before the stop line in both scenarios, without and with warning signs, compared to the hardest braking from 25 to 0 ms^-1 produced under the no motion cues conditions. Moreover, the results showed that a combination of the mild motion conditions and warning signs led to an underestimation of the actual speed and a greater fixation of the gaze on the speedometer. Questionnaire data revealed that 95% of the participants did not suffer from motion sickness symptoms, yet participants' preferences did not indicate that they were aware of the impact of simulator conditions on their driving behavior.

Lane-change intention prediction using eye-tracking technology: A systematic review

The aim of this study is to identify the best practices and future research directions for driver lane-change intention (DLCI) prediction using eye-tracking technologies based on a systematic literature review. We searched five academic literature databases and then conducted an in-depth review, structured coding, and analysis of 40 relevant articles. The literature on DLCI prediction is summarized in terms of input features, feature extraction and prediction time windows, labeling methods, and machine learning algorithms. The results show that eye tracking data features along with other data sources can be useful inputs for the prediction of DLCI. Major challenges in this line of research include determining the optimal time window for feature extraction and developing and evaluating the appropriate machine learning algorithm. Suggestions for future research and practice for DLCI prediction in intelligent vehicles are discussed.

Investigating the effect of marking and delineation treatments on driver behavior at highway exit gore areas

Highway exit gore areas are common and essential components of highway networks everywhere. Drivers need to navigate, decelerate, and change lanes at exit gore areas through specified and compacted geometry. This makes exit gore areas potentially crash-prone locations. Studies analyzed factors contributing to crashes at exit gore areas. Although marking and delineation techniques can play a major role in enhancing safety, traffic control devices that serve this end were not given sufficient attention in recent research. Furthermore, the currently used marking and delineation treatments are widely different around the world and deploy devices of various forms, shapes, sizes, and colors. The Flemish Agency for Roads & Traffic (AWV) launched a study to explore the feasibility of adding colored and sizeable eye-catching objects, mounted or grounded exactly at the physical nose, to attract driver's attention and improve driving performance. The eye-tracking and driving behavior of 49 Belgian drivers was investigated in a driving simulator. Participants were also queried about the conspicuity of the control devices and their personal preferences. The results were univocal for situations including a guardrail. Using two mounted panels together, i.e., the horizontal and the vertical, scored better on all levels than the horizontal panel alone. The bigger surface size of the traffic control device significantly improved driving performance and was also favored by the participants. Situations without a guardrail lacked such clear results, which were mixed depending on the measure at hand. A larger size of a grounded object-marker, again, improved driver's performance. Findings concerning the colors red or green appeared to favor red, although this was less univocal. These findings are aimed to spark ideas for further research and to assist practitioners and policy-makers in better designing exit gore areas while achieving more consistency and safety.

Proof of Concept of Novel Visuo-Spatial-Motor Fall Prevention Training for Old People

Falls in the geriatric population are one of the most important causes of disabilities in this age group. Its consequences impose a great deal of economic burden on health and insurance systems. This study was conducted by a multidisciplinary team with the aim of evaluating the effect of visuo-spatial-motor training for the prevention of falls in older adults. The subjects consisted of 31 volunteers aged 60 to 92 years who were studied in three groups: (1) A group under standard physical training, (2) a group under visuo-spatial-motor interventions, and (3) a control group (without any intervention). The results of the study showed that visual-spatial motor exercises significantly reduced the risk of falls of the subjects.

A low-cost, high-performance video-based binocular eye tracker for psychophysical research

We describe a high-performance, pupil-based binocular eye tracker that approaches the performance of a well-established commercial system, but at a fraction of the cost. The eye tracker is built from standard hardware components, and its software (written in Visual C++) can be easily implemented. Because of its fast and simple linear calibration scheme, the eye tracker performs best in the central 10 degrees of the visual field. The eye tracker possesses a number of useful features: (1) automated calibration simultaneously in both eyes while subjects fixate four fixation points sequentially on a computer screen, (2) automated realtime continuous analysis of measurement noise, (3) automated blink detection, (4) and realtime analysis of pupil centration artifacts. This last feature is critical because it is known that pupil diameter changes can be erroneously registered by pupil-based trackers as a change in eye position. We evaluated the performance of our system against that of a wellestablished commercial system using simultaneous measurements in 10 participants. We propose our low-cost eye tracker as a promising resource for studies of binocular eye movements.

The Effects of an In-vehicle Collision Warning System on Older Drivers' On-road Head Movements at Intersections

With age might come a decline in crucial driving skills. The effect of a collision warning system (CWS) on older drivers' head movements behavior at intersections was examined. Methods: Twenty-six old-adults, between 55 and 64 years of age, and 16 Older drivers between 65 and 83 years of age, participated in the study. A CWS (Mobileye Inc.) and a front-back in-vehicle camera (IVC) were installed in each of the participants' own vehicles for 6 months. The CWS was utilized to identify unsafe events during naturalistic driving situations, and the IVC was used to capture head direction at intersections. The experimental design was conducted in three phases (baseline, intervention, and carryover), 2 months each. Unsafe events were recorded by the CWS during all phases of the study. In the second phase, the CWS feedback was activated to examine its effect on drivers' head movement' behavior at intersections. Results: Older drivers (65+) drove significantly more hours in total during the intervention phase (M = 79.1 h, SE = 10) than the baseline phase (M = 39.1 h, SE = 5.3) and the carryover phase (M = 37.7 h, SE = 5.4). The study revealed no significant differences between the head movements of older and old-adult drivers at intersections. For intersection on the left direction, a significant improvement in drivers' head movements' behavior was found at T-junctions, turns and four-way intersections from phase 1 to phase 3 (p < 0.01), however, two intersection types presented a decrease along the study phases. The head movements' behavior at roundabouts and merges was better at phase 1 compared to phase 3 (p < 0.01). There was no significant reduction of the mean number of CWS unsafe events across the study phases. Conclusions: The immediate feedback provided by the CWS was effective in terms of participants' head movements at certain intersections but was harmful in others. However, older drivers drove many more hours during the active feedback phase, implying that they trusted the system. Therefore, in the light of this complex picture, using the technological feedback with older drivers should be followed with an additional mediation or follow-up to ensure safety.