A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure

TL;DR

This paper introduces a proactive system using deep learning to predict collision risk areas at crosswalks, aiming to prevent pedestrian accidents by analyzing vehicle and pedestrian trajectories from CCTV footage.

Contribution

It presents a novel predictive collision risk estimation method utilizing deep LSTM networks for trajectory prediction in urban surveillance environments.

Findings

System successfully predicts collision risk levels in real-world scenarios.

Application in Osan city demonstrates practical feasibility.

Provides a proactive warning mechanism for pedestrian safety.

Abstract

Road traffic accidents, especially vehicle pedestrian collisions in crosswalk, globally pose a severe threat to human lives and have become a leading cause of premature deaths. In order to protect such vulnerable road users from collisions, it is necessary to recognize possible conflict in advance and warn to road users, not post facto. A breakthrough for proactively preventing pedestrian collisions is to recognize pedestrian's potential risks based on vision sensors such as CCTVs. In this study, we propose a predictive collision risk area estimation system at unsignalized crosswalks. The proposed system applied trajectories of vehicles and pedestrians from video footage after preprocessing, and then predicted their trajectories by using deep LSTM networks. With use of predicted trajectories, this system can infer collision risk areas statistically, further severity of levels is divided as danger, warning, and relative safe. In order to validate the feasibility and applicability of the proposed system, we applied it and assess the severity of potential risks in two unsignalized spots in Osan city, Korea.