Earticle

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

키워드

저자

• 임병철 [ Byung-Chul Yim | Member, Researcher, Division of AI Mobility, Korea Automotive Technology Institute ] Corresponding Author
• 윤득선 [ Duk-Sun Yun | Member,Senior Researcher, Division of AI Mobility, Korea Automotive Technology Institute ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620