한국기계기술학회지 제23권 제5호 (p.828-833)

시뮬레이션 환경에서의 화물차 제동거리 예측 모델 연구

A Study on Prediction Model of Truck Braking Distance in Simulation Environment
키워드 :
화물차,Freight car,Braking distance,제동거리,Machine learning,기계학습

초록

Recently, traffic accidents have continued to occur due to the failure to secure a safe distance for trucks. Unlike passenger cars, freight cars have a large fluctuation in the weight of the vehicle's shaft depending on the load, and the fatality of accidents and the possibility of accidents are high. In this study, a braking distance prediction model according to the driving speed and loading weight of a three-axis truck was implemented to prevent a forward collision accident. Learning data was generated based on simulation, and a prediction model based on machine learning was implemented to finally verify accuracy. The extra trees algorithm was selected based on the most frequently used R2 Score among regression analyses, and the accuracy of the braking distance prediction model was 98.065% through 10 random scenarios.