Browse > Article
http://dx.doi.org/10.17661/jkiiect.2020.13.5.323

The road roughness based Braking Pressure Calculation System(BPCS) for an Autonomous Vehicle Stability  

Son, Su-Rak (Department of Software Engineering, Catholic kwandong University)
Lee, Byung-Kwan (Department of Software Engineering, Catholic kwandong University)
Sim, Son-Kweon (Department of Software Engineering, Catholic kwandong University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.13, no.5, 2020 , pp. 323-330 More about this Journal
Abstract
This paper proposes the road roughness based Braking Pressure Calculation System(BPCS) for an Autonomous Vehicle Stability. The system consists of an image normalization module that processes the front image of a vehicle to fit the input of the random forest, a Random Forest based Road Roughness Classification Module that distinguish the roughness of the road on which the vehicle is travelling by using the weather information and the front image of a vehicle as an input, and a brake pressure control module that modifies a friction coefficient applied to the vehicle according to the road roughness and determines the braking strength to maintain optimal driving according to a vehicle ahead. To verify the efficiency of the BPCS experiment was conducted with a random forest model. The result of the experiment shows that the accuracy of the random forest model was about 2% higher than that of the SVM, and that 7 features should be bagged to make an accurate random forest model. Therefore, the BPCS satisfies both real-time and accuracy in situations where the vehicle needs to brake.
Keywords
Brake pressure calculation; Random Forest; Road roughness classification; Region of Interest; Vehicle friction coefficient;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 "자율주행 차 사고, 뒤따르던 일반 차와 충돌 많았다." 김영주, https://news.joins.com/ article/23718449
2 Nguyen Manh Cuong, Jaesung Lee, "Automatic Detection of ROI for Vehicle Positioning", Proceedings of Symposium of the Korean In stitute of communications and Information Sciences, pp. 164-165, June, 2016
3 Lee, Seung-Hyun, Kim, Tae-Dong, Yi, Kang, Jung, Kyeong-Hoon, "Hypothesis Generation for Vehicle Detection by Combining Shadow and Edge", Conference of the Korean Society Of Broad Engineers, pp. 316-319, June, 2016
4 Hyun-cheol Yoon, Ju Yong Choi, "Transportation vehicle active safety warning systems developed algorithms". Fall Conference and Exhibition of the Korean Society Of Automotive Engineers, pp. 734-735, November, 2014
5 Jongcherl Park, Hojun Lee, Kyongsu Yi, "Cut-in Intension Inference based on Human Driving Data Analysis using Random Forest Method", The Korean Society of Mechanical Engineers 2018 Conference, pp. 1703-1708, December, 2018
6 Duyoung Heo, Sang Jun Kim, Choong Sub Kwak, Jae-Yeal Nam, Byoung Chul Ko,"Vehicle Headlight and Taillight Recognition in Nighttime using Low-Exposure Camera and Wavelet-based Random Forest", JOURNAL OF BROADCAST ENGINEERING Vol.22, No.3, ,pp. 282-294, May, 2017   DOI
7 Chanyong Choi, Hunki Kim, Young Cheul Kim, Sang-su Kim, "Prediction of Track Quality Index (TQI) Using Vehicle Acceleration Data based on Machine Learning", Journal of Korean Geosynthetics Society, Vol.19, No.1, pp. 45-53, March, 2020
8 Lynn B. Fricke, Traffic Accident Reconstruction, pp. 62-114, Northwest- ern University Traffic Institute, 1990
9 Kwangseub Kim, Wang Maosen, Naktak Jung, Seongmo Yang, Sehoon Yoo, Daeseong Gi, Myungwon Suh, "A Study on Drowsy Driving Behavior Detection Based on Driving Information", Spring Conference of the Korean Society Of Automotive Engineers, pp.702-705, May, 2015