실천공학교육논문지 (Journal of Practical Engineering Education)

  • 제14권3호
  • /
  • Pages.593-600
  • /
  • 2022
  • /
  • 2288-405X(pISSN)
  • /
  • 2288-4068(eISSN)
한국실천공학교육학회 (Korean Institute for Pratical Engineering Education)
권호 표지
DOI QR코드

DOI QR Code

전방 차량의 횡간 이동 예측을 위한 차선 간 거리 측정 방법

Inter-Lane Distance Measurement Method for Predicting the Lateral Movement of the Vehicle in Front

  • 용성중 (한국기술교육대학교 컴퓨터공학과) ;
  • 박효경 (한국기술교육대학교 컴퓨터공학과) ;
  • 이서영 (한국기술교육대학교 컴퓨터공학과) ;
  • 유연휘 (한국기술교육대학교 컴퓨터공학과) ;
  • 문일영 (한국기술교육대학교 컴퓨터공학과)
  • Sung-Jung Yong (Department of Computer Science and Engineering, Korea University of Technology and Education) ;
  • Hyo-Gyeong Park (Department of Computer Science and Engineering, Korea University of Technology and Education) ;
  • Seo-young Lee (Department of Computer Science and Engineering, Korea University of Technology and Education) ;
  • Yeon-Hwi You (Department of Computer Science and Engineering, Korea University of Technology and Education) ;
  • Il-Young Moon (Department of Computer Science and Engineering, Korea University of Technology and Education)
  • 투고 : 2022.11.25
  • 심사 : 2022.12.15
  • 발행 : 2022.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

자율주행 차량에는 라이다, 레이더, 카메라 등 다양한 센서들이 융합되어 활용되고 있다. 특히 라이더 및 레이더는 고가의 장비로 자율주행 자동차의 대중화를 위해 해결해야 하는 부분으로 고가의 장비를 대체할 수 있는 연구가 지속적으로 이루어지고 있다. 본 논문에서는 비용면에서 저가이면서 손쉽게 장착할 수 있는 단일 카메라를 이용하여 주행 차량의 전방 측면 차량 바퀴와 인접 차선을 감지하고 거리를 추정하는 방법을 제안하였다. 제안된 방법은 입력 영상을 통해 프레임 추출 후 프레임 이미지에서 차선과 바퀴를 검출하고 거리를 측정하여 실제 도로 환경에서 실측 된 거리와 비교하였고, 오차범위 ±3cm 안에서 비교적 정확히 거리를 산출할 수 있었다. 이를 통해 자율주행 자동차의 비용 절감 또는 라이다, 레이더 센서의 고장으로 대체 가능한 수단으로 활용할 수 있을 것으로 판단된다.

Various sensors such as lidar, radar, and camera are fused and used in autonomous vehicles. Rider and radar sensors are difficult to popularize because they are expensive equipment. In order to popularize autonomous vehicles, research that can replace expensive equipment is continuously being conducted. In this paper, we use a single camera that is inexpensive and can be easily mounted. We propose a method for detecting the wheels and adjacent lanes of a front-side vehicle of a driving vehicle and estimating distances. Our proposed method detects lanes and wheels from frame images after frame extraction via input images. In addition, the distance is measured and compared with the actual distance measured in the actual road environment. The distance could be calculated relatively accurately within the error range of ± 3 cm. Through this, it is expected that the camera can be used as an alternative means when the cost of autonomous vehicles is reduced or when the lidar or radar sensor fails.

키워드

과제정보

본 과제는 2022년도 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 지자체-대학 협력기반 지역혁신 사업의 결과입니다(2021RIS-004).

참고문헌

  1. S. Kim, Y. Kim, H. Jeon, D. Kum, and K. Lee, "Autonomous driving technology trend and future outlook: powered by artificial intelligence," Transaction of the Korean Society of Automotive Engineers, vol. 30, no. 10, pp. 819-830, October 2022. https://doi.org/10.7467/KSAE.2022.30.10.819
  2. J. Zhao, J. Zhang, and B. Zhu, "Development and verification of the tire/road friction estimation algorithm for antilock braking system," Mathematical Problems in Engineering, vol. 2014, no. 786492, pp. 1-15, September 2014.
  3. S. H. Han, S. Chae, J. H. Park, S. A. Hassan, T. Rahim, and S. Y. Shin, "Video-based traffic accident prevention safety system using deep learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 8, pp. 1399-1406, August 2020. https://doi.org/10.7840/kics.2020.45.8.1399
  4. Y. Ko, Y. Lee, S. Azam, F. Munir, M. Jeon, and W. Pedrycz, "Key points estimation and point instance segmentation approach for lane detection," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 8949-8958, July 2022. https://doi.org/10.1109/TITS.2021.3088488
  5. T. Zheng, H. Fang, Y. Zhang, W. Tang, Z. Yang, H. Liu, and D. Cai, "RESA: Recurrent feature-shift aggregator for lane detection," in Proceedings of the 35th AAAI Conference on Artificial Intelligence, Online, pp. 3547-3554, 2021.
  6. C. Hu, S. Hudson, M. Ethier, M. Al-Sharman, D. Rayside, and W. Melek, "Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving," in Proceeding of the 2022 IEEE Intelligent Vehicles Symposium (IV), Aachen: Germany, pp. 457-463, 2022.
  7. K. H. An, S. W. Lee, W. Y. Han, and J. C. Son, "Technology trends of self-driving vehicles," 2013 Electronics and Telecommunications Trends, vol. 28, no. 4, pp. 35-44, August 2013.
  8. Y. Sim and S. Kwon, "Standardization trend of autonomous-driving vehicle system", Auto Journal: Journal of the Korean Society of Automotive Engineers, vol. 41, no. 9, pp. 63-66, September 2019.
  9. J. C. Seo, S. T. Oh, and Y. K. Kim, "A study of curved lane detection based on dual sensor monitoring of LiDAR and camera," Transaction of the Korean Society of Automotive Engineers, vol. 29, no. 2, pp. 197-204, February 2021. https://doi.org/10.7467/KSAE.2021.29.2.197
  10. H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao, "Deep ordinal regression network for monocular depth estimation," in Proceeding of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City: UT, USA, pp. 2002-2011, 2018.
  11. J. S. Kim, "Effective road distance estimation using a vehicle-attached black box camera," Journal of the Korea Institute of Information and Communication Engineering, vol. 19, no. 3, pp. 651-658, March 2015. https://doi.org/10.6109/jkiice.2015.19.3.651
  12. OpenCV Docs [Internet]. Available: https://docs.opencv.org/3.4/
  13. T. Taoka, M. Manabe, and M. Fukui, "An efficient curvature lane recognition algorithm by piecewise linear approach," in Proceeding of the 65th 2007 Vehicular Technology Conference (VTC 2007), Dublin: IE, pp. 2530-2534, 2007.
  14. Q. Lin, Y. Han, and H. Hahn, "Real-Time Lane Departure Detection Based on Extended Edge-Linking Algorithm", in proceeding of the 2010 Second International Conference on Computer Research and Development, Kuala Lumpur: MY, pp. 725-130, 2010.