The Visual Scanning Behavior and Mental Workload of Drivers at Prairie Highway Intersections With Different Characteristics

Abstract

Highway intersections are crash prone locations, and drivers' improper attention allocation and sudden increase of mental workload are main contributing factors. To explore the visual scanning characteristics and mental workload of drivers at prairie highway intersections with different characteristics, an on-road driving test was taken at 6 intersections scattered on a typical prairie highway with 3 different shapes and 2 different priority rules, and drivers' eye movement and ECG data were collected. The results show that there is significant difference in fixation allocation and transferring among intersections with different shapes and priority rules. The X-shaped intersection without the right of way shows the longest average fixation duration and the most gaze shifts between the drivers' lane and the intersection road, whereas the three-way intersection with the right of way shows the shortest fixation duration and the gaze transfer path is relatively simple and the transfers are less. The mental workload implies almost the same conclusion, with the X-shaped intersection without right of way shows the highest mental workload which is manifested by largest heart rate growth rate and the lowest RMSSD, whereas the three-way with priority of way shows the opposite trend. This study revealed that both intersection types and priority rule made differences in drivers' scanning behavior and mental workload at prairie highway intersections, which can support the development of appropriate countermeasures regarding the applications in advanced driver assistance systems and the design of intersection constructions to warn drivers about potential critical events at different types of intersections.

A Semantic Hybrid Temporal Approach for Detecting Driver Mental Fatigue

“…In conclusion, although our study introduces a novel methodology for detecting driver mental fatigue on long monotonous highways using a hybrid approach, it is crucial to acknowledge the inherent limitations. The use of a low-fidelity driving simulator and a small sample size, along with the inclusion of complex road intersections [47], presents challenges to generalization. Looking ahead, future studies with larger and more diverse participant groups, as well as higher-fidelity driving simulators that include challenging and complex roads, roundabouts, and junctions, are imperative to validate and enhance the robustness of our proposed methodology.…”