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http://dx.doi.org/10.7472/jksii.2022.23.2.53

The Road condition-based Braking Strength Calculation System for a fully autonomous driving vehicle  

Son, Su-Rak (Department of Software, Catholic Kwandong Univ.)
Jeong, Yi-Na (Department of Software, Catholic Kwandong Univ.)
Publication Information
Journal of Internet Computing and Services / v.23, no.2, 2022 , pp. 53-59 More about this Journal
Abstract
After the 3rd level autonomous driving vehicle, the 4th and 5th level of autonomous driving technology is trying to maintain the optimal condition of the passengers as well as the perfect driving of the vehicle. However current autonomous driving technology is too dependent on visual information such as LiDAR and front camera, so it is difficult to fully autonomously drive on roads other than designated roads. Therefore this paper proposes a Braking Strength Calculation System (BSCS), in which a vehicle classifies road conditions using data other than visual information and calculates optimal braking strength according to road conditions and driving conditions. The BSCS consists of RCDM (Road Condition Definition Module), which classifies road conditions based on KNN algorithm, and BSCM (Braking Strength Calculation Module), which calculates optimal braking strength while driving based on current driving conditions and road conditions. As a result of the experiment in this paper, it was possible to find the most suitable number of Ks for the KNN algorithm, and it was proved that the RCDM proposed in this paper is more accurate than the unsupervised K-means algorithm. By using not only visual information but also vibration data applied to the suspension, the BSCS of the paper can make the braking of autonomous vehicles smoother in various environments where visual information is limited.
Keywords
Road conditions; K Nearest Neighbor algorithms; Breaking strength; Neural Networks; Fully Autonomous Driving;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 SAE, "SAE International Releases Updated Visual Chart for Its "Levels of Driving Automation," Standard for Self-Driving Vehicles", 2018, https://www.sae.org/news/press-room/2018/12/sae-international-releases-updated-visual-chart-for-its-%E2%80%9Clevels-of-driving-automation%E2%80%9D-standard-for-self-driving-vehicles
2 Steinhaus, H. "Sur la division des corps materiels en parties". Bull. Acad. Polon. Sci., Vol 4, No12, pp. 801-804, 1957.
3 MacQueen, J. B. "Some Methods for classification and Analysis of Multivariate Observations," Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. pp. 281-297, 1967.
4 Mohammad Arbabpour Bidgoli, Amir Golroo, Hamid Sheikhzadeh Nadjar, Ali Ghelmani Rashidabad, Mohammad Reza Ganji, "Road roughness measurement using a cost-effective sensor-based monitoring system, " Automation in Construction, Vol 104, pp. 140-152, 2019. https://doi.org/10.1016/j.autcon.2019.04.007   DOI
5 Fouzi Harrou, Abdelhafid Zeroual, Ying Sun, "Traffic congestion monitoring using an improved kNN strategy," Measurement, Vol 165, 107534, 2020. https://doi.org/10.1016/j.measurement.2020.107534   DOI
6 Y. Yang, "Expert Network : Effective and efficient learning from human decisions in text categorization and retriecal," In 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1994
7 Young-Hwan Choi, Hongrae Kim, & Min Hong, "A Road Luminance Measurement Application based on Android," Journal of Internet Computing and Services, Vol 16, No2, pp. 49-56, 2015. https://doi.org/10.7472/jksii.2015.16.2.49.   DOI
8 김용균, "다시 주목받기 시작하는 자율주행차", S&T 혠, Vol 184, 2021. https://now.k2base.re.kr/portal/issue/ovseaIssued/view.do?poliIsueId=ISUE_000000000000977&menuNo=200&pageIndex=1
9 Altman, N. S., "An introduction to kernel and nearest-neighbor nonparametric regression," The American Statistician, Vol 46, No3, pp.175-185, 1992. https://doi.org/10.1080/00031305.1992.10475879.   DOI