A Real-time Evaluation Framework for Pedestrian's Potential Risk at Non-Signalized Intersections Based on Predicted Post-Encroachment Time

TL;DR

This paper introduces a real-time evaluation framework using computer vision and deep learning to predict pedestrian risks at non-signalized intersections, focusing on the novel P-PET safety measure and pedestrian classification.

Contribution

It develops a real-time risk evaluation framework with a new safety indicator, P-PET, and tailored criteria for different pedestrian categories, enhancing accuracy and explainability.

Findings

Effective identification of potential risks using P-PET

Framework demonstrates feasibility for real-time application

Improved risk assessment across pedestrian categories

Abstract

Addressing pedestrian safety at intersections is one of the paramount concerns in the field of transportation research, driven by the urgency of reducing traffic-related injuries and fatalities. With advances in computer vision technologies and predictive models, the pursuit of developing real-time proactive protection systems is increasingly recognized as vital to improving pedestrian safety at intersections. The core of these protection systems lies in the prediction-based evaluation of pedestrian's potential risks, which plays a significant role in preventing the occurrence of accidents. The major challenges in the current prediction-based potential risk evaluation research can be summarized into three aspects: the inadequate progress in creating a real-time framework for the evaluation of pedestrian's potential risks, the absence of accurate and explainable safety indicators that can represent the potential risk, and the lack of tailor-made evaluation criteria specifically for each category of pedestrians. To address these research challenges, in this study, a framework with computer vision technologies and predictive models is developed to evaluate the potential risk of pedestrians in real time. Integral to this framework is a novel surrogate safety measure, the Predicted Post-Encroachment Time (P-PET), derived from deep learning models capable to predict the arrival time of pedestrians and vehicles at intersections. To further improve the effectiveness and reliability of pedestrian risk evaluation, we classify pedestrians into distinct categories and apply specific evaluation criteria for each group. The results demonstrate the framework's ability to effectively identify potential risks through the use of P-PET, indicating its feasibility for real-time applications and its improved performance in risk evaluation across different categories of pedestrians.