Road Surface Data Collection and Analysis using A2B Communication in Vehicles from Bearings and Deep Learning Research

• 영문명: Road Surface Data Collection and Analysis using A2B Communication in Vehicles from Bearings and Deep Learning Research
• 발행기관: 한국인공지능학회
• 저자명: 김영민(Young-Min KIM) 황재용(Jae-Yong HWANG) 강선경(Sun-Kyoung KANG)
• 간행물 정보: 『인공지능연구』Vol.11 No. 4, 21~27쪽, 전체 7쪽
• 주제분류: 복합학 > 과학기술학
• 파일형태: PDF
• 발행일자: 2023.12.31

영문 초록

This paper discusses a deep learning-based road surface analysis system that collects data by installing vibration sensors on the 4-axis wheel bearings of a vehicle, analyzes the data, and appropriately classifies the characteristics of the current driving road surface for use in the vehicle's control system. The data used for road surface analysis is real-time large-capacity data, with 48K samples per second, and the A2B protocol, which is used for large-capacity real-time data communication in modern vehicles, was used to collect the data. CAN and CAN-FD commonly used in vehicle communication, are unable to perform real-time road surface analysis due to bandwidth limitations. By using A2B communication, data was collected at a maximum bandwidth for real-time analysis, requiring a minimum of 24K samples/sec for evaluation. Based on the data collected for real-time analysis, performance was assessed using deep learning models such as LSTM, GRU, and RNN. The results showed similar road surface classification performance across all models. It was also observed that the quality of data used during the training process had an impact on the performance of each model.

목차

1. Introduction

2. Literature Review

3. Research Methods

4. Results and Discussion

5. Conclusions

References

키워드

Deep learning-based road surface analysis Data collection A2B Real-time analysis system Vibration sensoror