A High-Risk Event Detection Algorithm Based on Vehicle Interactions Prediction at Urban Intersections

JO, Young, LEE, Seolyoung, OH, Cheol, SUH, Wonho, KIM, Hyoungsoo

doi:10.7470/jkst.2022.40.2.245

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2022 Vol.40, Issue 2 Preview Page Next Page

Article

A High-Risk Event Detection Algorithm Based on Vehicle Interactions Prediction at Urban Intersections 교차로 차량 주행안전성 예측 기반 위험상황 검지 기술 개발

30 April 2022. pp. 245-259

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Abstract

개별차량의 주행안전성 예측을 통해 위험상황을 검지하는 것은 능동적이고 효과적인 교통사고 예방을 위한 선제적 교통안전 분석의 중요 기술 중 하나이다. 본 연구의 목적은 차량 주행궤적자료를 이용하여 차량 간 상호작용 예측 기반의 위험상황 검지 알고리즘을 개발하는 것이다. 차량 간 상호작용은 Long-Short Term Memory(LSTM) 기반 위치 예측 모델 개발을 통해 위험상황을 정량화함으로써 평가되었다. 차량 위치 예측의 경우 time scale parameter와 LSTM 하이퍼파라미터를 연계하여 위치 예측 정확도가 우수한 최적화된 모델을 구축하였다. 예측된 위치 정보는 차량 간 상호작용 분석을 통한 사고개연성을 추정할 수 있는 High-risk Event Indicator(HEI)를 산출하는데 적용되어 위험상황 검지에 활용되었다. 제안된 알고리즘은 도심부 교차로에서 수집된 영상검지 기반의 차량 주행궤적자료를 이용하여 적용성을 평가하였다. 차량 위치 예측 결과, rolling horizon이 5초이고 prediction horizon이 1초인 경우에 대해 최적화된 LSTM 모델의 Mean Absolute Percentage Error(MAPE)는 x축 위치 1.10%, y축 위치 3.74%로 도출되었다. 예측된 위치에서 위험상황 검지 정확도를 도출하기 위한 성능척도로 correct detection rate(CDR)를 정의하였다. 분석 결과, CDR은 HEI 임계값 1.5-3초를 기준으로 94% 이상으로 산출되었다. 차량 간 상호작용 예측을 통한 위험상황 검지 기술은 자율주행차량이 교차로를 안전하게 통과하기 위한 I2V 무선통신 기반의 선제적 교통안전 정보로 활용될 수 있을 것으로 기대된다.

키워드

위험상황 검지

교차로 주행안전성

LSTM

선제적 교통 안전성

차량간 상호작용

An important technology for proactive traffic safety analytics, which is a backbone of preventing traffic crashes, is to identify hazardous situations based on vehicle interactions prediction. The purpose of this study is to develop a high-risk event detection algorithm by analyzing predicted vehicle interactions using trajectory data. Vehicle interactions were evaluated in terms of quantifying crash risks by the proposed long-short term memory based vehicle position prediction model. The vehicle position prediction model is optimized for a parameter set with superior accuracy by combining the time scale parameters and the prediction model hyperparameters. A high-risk event indicator (HEI) was then derived based on an analysis of vehicle interactions using predicted position data. The applicability of the proposed algorithm was evaluated based on trajectory data obtained from vehicle image tracking technology at an urban intersection. The prediction model developed in this study was able to predict the x-axis and y-axis vehicle positions after 1 seconds with mean absolute percentage errors of approximately 1% and 3%, respectively. In addition, the correct detection rate (CDR) was defined as a performance measure to derive the accuracy of high-risk events using the proposed HEI. Performance evaluations of the proposed algorithm show promising results for actual implementation in practice, where a CDR of up to 94% can be achieved. The infrastructure-based detection of high-risk events by predicting vehicle interactions would be useful in supporting autonomous vehicles crossing intersections safely via I2V wireless communication.

Keywords

high-risk event detection

intersection safety

LSTM

proactive traffic safety

vehicle interactions

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Information

Publisher :Korean Society of Transportation
Publisher(Ko) :대한교통학회
Journal Title :Journal of Korean Society of Transportation
Journal Title(Ko) :대한교통학회지
Volume : 40
No :2
Pages :245-259
Received Date :2021. 11. 24
Revised Date :2021. 12. 21
Accepted Date : 2022. 02. 04
DOI :https://doi.org/10.7470/jkst.2022.40.2.245

Journal of Korean Society of Transportation ISSN:1229-1366(Print) 2234-4217(Online) 대한교통학회지

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