심층 신경망을 이용한 승차감 평가 모델 개발 및 검증 (A Study on the Development and Verification of a Ride Comfort Evaluation Model Using Deep Neural Networks)

서지정보

· 발행기관 : 한국기계기술학회
· 수록지 정보 : 한국기계기술학회지 / 27권 / 2호 / 332 ~ 336페이지
· 저자명 : 이동필, 김병삼

초록

Ride comfort is a key factor in vehicle performance, yet traditional evaluations often rely on subjective methods, leading to inconsistencies. This study presents a deep neural network (DNN)-based model trained on real-world driving data to objectively assess ride comfort. The model’s accuracy is validated using RMS, VDV, and Crest Factor based on ISO 2631. Results show that the DNN effectively captures nonlinear vibration characteristics and offers reliable predictions. This highlights the potential of AI in improving ride comfort assessment.

영어초록

Ride comfort is a key factor in vehicle performance, yet traditional evaluations often rely on subjective methods, leading to inconsistencies. This study presents a deep neural network (DNN)-based model trained on real-world driving data to objectively assess ride comfort. The model’s accuracy is validated using RMS, VDV, and Crest Factor based on ISO 2631. Results show that the DNN effectively captures nonlinear vibration characteristics and offers reliable predictions. This highlights the potential of AI in improving ride comfort assessment.

참고자료

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