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시뮬레이션 환경에서의 화물차 제동거리 예측 모델 연구

원문정보

A Study on Prediction Model of Truck Braking Distance in Simulation Environment

임병철, 윤득선

피인용수 : 0(자료제공 : 네이버학술정보)

초록

영어

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.

목차

ABSTRACT
1. 서론
2. 학습 데이터 셋 생성
2.1 주행 시뮬레이션 시나리오
2.2 주행 취득 데이터
2.3 학습 데이터 셋 생성
3. 학습 데이터 셋 분석
3.1 입력 변수에 따른 제동거리 변화
3.2 입력 변수 중요도
4. 제동거리 예측 모델 평가
4.1 제동거리 예측 모델 생성
4.2 제동거리 예측 모델 평가
4.3 제동거리 예측 모델 선정
5. 제동거리 예측 모델 성능 검증
5.1 성능 검증 시나리오
5.2 성능 검증 결과
6. 결론
후기
References

저자정보

  • 임병철 Byung-Chul Yim. Member, Researcher, Division of AI Mobility, Korea Automotive Technology Institute
  • 윤득선 Duk-Sun Yun. Member,Senior Researcher, Division of AI Mobility, Korea Automotive Technology Institute

참고문헌

자료제공 : 네이버학술정보

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