THE EFFECTS OF GUIDANCE METHOD ON DETECTION AND SCANNING AT INTERSECTIONS - A PILOT STUDY

Automatic processing of gaze movements to quantify gaze scanning behaviors in a driving simulator

Eye and head movements are used to scan the environment when driving. In particular, when approaching an intersection, large gaze scans to the left and right, comprising head and multiple eye movements, are made. We detail an algorithm called the gaze scan algorithm that automatically quantifies the magnitude, duration, and composition of such large lateral gaze scans. The algorithm works by first detecting lateral saccades, then merging these lateral saccades into gaze scans, with the start and end points of each gaze scan marked in time and eccentricity. We evaluated the algorithm by comparing gaze scans generated by the algorithm to manually marked "consensus ground truth" gaze scans taken from gaze data collected in a high-fidelity driving simulator. We found that the gaze scan algorithm successfully marked 96% of gaze scans and produced magnitudes and durations close to ground truth. Furthermore, the differences between the algorithm and ground truth were similar to the differences found between expert coders. Therefore, the algorithm may be used in lieu of manual marking of gaze data, significantly accelerating the time-consuming marking of gaze movement data in driving simulator studies. The algorithm also complements existing eye tracking and mobility research by quantifying the number, direction, magnitude, and timing of gaze scans and can be used to better understand how individuals scan their environment.

Towards Wide Range Tracking of Head Scanning Movement in Driving

Gaining environmental awareness through lateral head scanning (yaw rotations) is important for driving safety, especially when approaching intersections. Therefore, head scanning movements could be an important behavioral metric for driving safety research and driving risk mitigation systems. Tracking head scanning movements with a single in-car camera is preferred hardware-wise, but it is very challenging to track the head over almost a 180° range. In this paper we investigate two state-of-the-art methods, a multi-loss deep residual learning method with 50 layers (multi-loss ResNet-50) and an ORB feature-based simultaneous localization and mapping method (ORB-SLAM). While deep learning methods have been extensively studied for head pose detection, this is the first study in which SLAM has been employed to innovatively track head scanning over a very wide range. Our laboratory experimental results showed that ORB-SLAM was more accurate than multi-loss ResNet-50, which often failed when many facial features were not in the view. On the contrary, ORB-SLAM was able to continue tracking as it doesn't rely on particular facial features. Testing with real driving videos demonstrated the feasibility of using ORB-SLAM for tracking large lateral head scans in naturalistic video data.

The Effects of Age and Central Field Loss on Head Scanning and Detection at Intersections

Purpose

Using a driving simulator, we quantified the effects of age and central field loss (CFL) on head scanning when approaching an intersection and investigated the role of inadequate head scanning in detection failures.

Methods

Participants with CFL (n = 20) and with normal vision (NV; n = 29), middle-aged (36–60 years) or older (67–87 years), drove along city routes with multiple intersections while head movements were recorded. The effects of age and CFL on scanning were analyzed at 32 intersections with stop/yield signs. The relationships between age, CFL, scanning, and detection were examined at four additional intersections with a pedestrian appearing on the far left.

Results

Older NV participants made fewer total scans than middle-aged NV participants and had smaller maximum scan magnitudes. Head scanning of older CFL and NV participants did not differ, but middle-aged CFL participants made fewer head scans, had higher rates of failing to scan, and made smaller head scans than middle-aged NV participants. For the older NV and both CFL groups, detection failures were high (≥58%); head scan magnitudes were 15° smaller when the pedestrian was not detected than when it was detected.

Conclusions

Both older NV and CFL participants exhibited head scanning deficits relative to middle-aged NV participants. Unexpectedly, however, it was the middle-aged CFL group that performed least well when scanning, a finding that warrants further investigation.

Translational Relevance

Failing to head scan sufficiently far at intersections may place older drivers and drivers with vision impairment at a higher risk for causing collisions.

The effects of age and cognitive load on peripheral-detection performance

Age-related declines in both peripheral vision and cognitive resources could contribute to the increased crash risk of older drivers. However, it is unclear whether increases in age and cognitive load result in equal detriments to detection rates across all peripheral target eccentricities (general interference effect) or whether these detriments become greater with increasing eccentricity (tunnel effect). In the current study we investigated the effects of age and cognitive load on the detection of peripheral motorcycle targets (at 5°–30° eccentricity) in static images of intersections. We used a dual-task paradigm in which cognitive load was manipulated without changing the complexity of the central (foveal) visual stimulus. Each image was displayed briefly (250 ms) to prevent eye movements. When no cognitive load was present, age resulted in a tunnel effect; however, when cognitive load was high, age resulted in a general interference effect. These findings suggest that tunnel and general interference effects can co-occur and that the predominant effect varies with the level of demand placed on participants' resources. High cognitive load had a general interference effect in both age groups, but the effect attenuated at large target eccentricities (opposite of a tunnel effect). Low cognitive load had a general interference effect in the older but not the younger group, impairing detection of motorcycle targets even at 5° eccentricity, which could present an imminent collision risk in real driving.