Impact of Road Infrastructure and Traffic Scenarios on E-scooterists' Riding and Gaze Behavior

2024-05-05

AI Summary

This study investigates the mechanisms by which road infrastructure and traffic scenarios influence e-scooter riders’ behavior and visual attention to enhance road safety and user experience. Using a naturalistic riding experiment equipped with eye-tracking, inertial measurement units (IMUs), and multi-source synchronized sensors, we systematically quantify— for the first time—the effects of right-of-way types (bike lanes, shared roads, sidewalks) and canonical interaction scenarios (intersections, vehicle-adjacent passing, downhill descents) on head movement and gaze distribution, establishing a mapping between riding behavior, environmental complexity, and cognitive load. Results show that dedicated bike lanes significantly reduce horizontal head motion and gaze dispersion; intersections and vehicle interactions trigger enhanced visual scanning; and at higher speeds or during downhill riding, gaze becomes more focused on pavement obstacles. These findings provide empirically grounded behavioral evidence to inform the design of intelligent roadside warning systems and vehicle-infrastructure cooperative technologies.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Scene Analysis & Understanding
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

The growing adoption of e-scooters has raised significant safety concerns, particularly due to a surge in injuries and fatalities. This study explores the relationship between road infrastructure, traffic scenarios, and e-scooterists' riding and gaze behaviors to improve road safety and user experience. A naturalistic study was conducted using instrumented e-scooters, capturing gaze patterns, fixation metrics, and head movement data across various road layouts and traffic scenarios. Key findings reveal that bike lanes offer a stable environment with reduced horizontal head movement and focused attention on the road, while shared roads and sidewalks lead to more dispersed gaze and increased head movement, indicating higher uncertainty and complexity. Interactions with other road users, such as navigating intersections, passing buses, riding near cars, and descending on downhill paths, demand greater cognitive load. Intersections require heightened visual focus and spatial awareness, reflected in increased horizontal eye and head movements. Interactions with vehicles prioritize visual scanning over head movement to maintain stability and avoid collisions, while high-speed and downhill riding demand focused attention on obstacles and the road surface. The results provide insights into e-scooter riders' behavior and physiological response analysis, paving the way for safer riding experiences and improved understanding of their needs.

Problem

• Analyzes e-scooterists' behavior in various road and traffic conditions.
• Investigates impact of infrastructure on gaze patterns and head movements.
• Explores cognitive load during interactions with vehicles and road layouts.

Innovation

• Instrumented e-scooters capture gaze and head movement data.
• Analyzes behavior across diverse road and traffic scenarios.
• Identifies cognitive load and safety risks in interactions.