Human-Centered Design for Connected Automation: Predicting Pedestrian Crossing Intentions

2025-08-28

AI Summary

This study addresses the challenges of pedestrian crossing intention prediction and insufficient safety in human-vehicle interaction under Level 5 autonomous driving systems (ADS). Extending the Theory of Planned Behavior, we incorporate four external factors—perceived safety, trust, compatibility, and comprehensibility—to formulate a pedestrian crossing decision-making model. Using data from 212 online questionnaires, we employ structural equation modeling to examine how perceived behavioral control, attitude, and social information influence crossing intention. Results indicate that perceived safety and comprehensibility are significant predictors of pedestrian crossing intention (p < 0.01), whereas trust and compatibility exhibit no statistically significant effect. These findings provide empirical support for designing external human–machine interfaces (eHMIs) and cooperative V2X communication strategies tailored to pedestrian behavior, thereby advancing human-centered vehicle–infrastructure cooperative systems.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Application Domains: Humanities & Computational Social Science

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Road traffic remains a leading cause of death worldwide, with pedestrians and other vulnerable road users accounting for over half of the 1.19 million annual fatalities, much of it due to human error. Level-5 automated driving systems (ADSs), capable of full self-driving without human oversight, have the potential to reduce these incidents. However, their effectiveness depends not only on automation performance but also on their ability to communicate intent and coordinate safely with pedestrians in the absence of traditional driver cues. Understanding how pedestrians interpret and respond to ADS behavior is therefore critical to the development of connected vehicle systems. This study extends the Theory of Planned Behavior (TPB) by incorporating four external factors (i.e. safety, trust, compatibility, and understanding) to model pedestrian decision-making in road-crossing scenarios involving level-5 ADSs. Using data from an online survey (n = 212), results show that perceived behavioral control, attitude, and social information significantly predict pedestrians' crossing intentions. External factors, particularly perceived safety and understanding, strongly influence these constructs. Findings provide actionable insights for designing external human-machine interfaces (eHMIs) and cooperative V2X communication strategies that support safe, transparent interactions between automated vehicles and pedestrians. This work contributes to the development of inclusive, human-centered connected mobility systems.

Problem

• Predicting pedestrian crossing intentions with automated vehicles
• Modeling pedestrian decision-making in road-crossing scenarios
• Designing communication strategies for vehicle-pedestrian interactions

Innovation

• Extends Theory of Planned Behavior with external factors
• Uses online survey data to model pedestrian decisions
• Designs eHMIs and V2X strategies for safe interactions