The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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A Research on V2I-based Accident Prevention System for the Prevention of Unexpected Accident of Autonomous Vehicle

자율주행 차량의 돌발사고 방지를 위한 V2I 기반의 사고 방지체계 연구

  • Han, SangYong (Graduate school of Autumotive Eng., Kookmin Univ.) ;
  • Kim, Myeong-jun (Graduate school of Autumotive Eng., Kookmin Univ.) ;
  • Kang, Dongwan (Graduate school of Autumotive Eng., Kookmin Univ.) ;
  • Baek, Sunwoo (Graduate school of Autumotive Eng., Kookmin Univ.) ;
  • Shin, Hee-seok (Graduate school of Autumotive Eng., Kookmin Univ.) ;
  • Kim, Jungha (College of Automotive Eng., Kookmin Univ.)
  • 한상용 (국민대학교 자동차공학전문대학원) ;
  • 김명준 (국민대학교 자동차공학전문대학원) ;
  • 강동완 (국민대학교 자동차공학전문대학원) ;
  • 백선우 (국민대학교 자동차공학전문대학원) ;
  • 신희석 (국민대학교 자동차공학전문대학원) ;
  • 김정하 (국민대학교 자동차융합대학)
  • Received : 2021.04.22
  • Accepted : 2021.06.09
  • Published : 2021.06.30
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Abstract

This research proposes the Accident Prevention System to prevent collision accident that can occur due to blind spots such as crossway or school zone using V2I communication. Vision sensor and LiDAR sensor located in the infrastructure of crossway somewhere like that recognize objects and warn vehicles at risk of accidents to prevent accidents in advance. Using deep learning-based YOLOv4 to recognize the object entering the intersection and using the Manhattan Distance value with LiDAR sensors to calculate the expected collision time and the weight of braking distance and secure safe distance. V2I communication used ROS (Robot Operating System) communication to prevent accidents in advance by conveying various information to the vehicle, including class, distance, and speed of entry objects, in addition to collision warning.

본 연구는 V2I통신을 이용하여 교차로 등의 사각지대로 인해 발생할 수 있는 충돌 사고를 예방하기 위한 충돌 방지체계를 제안한다. 교차로의 인프라에 위치한 Vision센서와 LiDAR센서가 물체를 인식하고 사고 위험이 있는 차량에게 경고함으로써 사고를 미연에 방지한다. 딥러닝 기반의 YOLOv4를 이용하여 교차로에 진입하는 물체를 인식하고 LiDAR 센서와의 Calibration을 통해 대상 물체와의 맨하탄 거리값을 이용하여 충돌 예상시간과 제동거리에 대한 가중치를 계산하고 안전거리를 확보한다. 차량-인프라간 통신은 ROS통신을 이용하였으며 충돌 경고 외에도 진입 물체의 Class, 거리, 진행속도 등의 다양한 정보를 차량에 전달함으로써 사고를 미연에 방지하고자 하였다.

Keywords

References

  1. Bento L. C. et al.(2012), "Intelligent traffic management at intersections supported by V2V and V2I communications," 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp.1495-1502.
  2. Bochkovskiy, A., Wang C. Y. and Mark Liao H. Y.(2020), "Yolov4: Optimal speed and accuracy of object detection," arXiv preprint arXiv:2004.10934.
  3. Debeunne C. and Vivet D.(2020), "A review of visual-LiDAR fusion based simultaneous localization and mapping," Sensors, vol. 20, no. 7, p.2068. https://doi.org/10.3390/s20072068
  4. Dickmann J. et al.(2016), "Automotive radar the key technology for autonomous driving: From detection and ranging to environmental understanding," 2016 IEEE Radar Conference (RadarConf), IEEE.
  5. Figueroa J. F. and Lamancusa J. S.(1992), "A method for accurate detection of time of arrival: Analysis and design of an ultrasonic ranging system," The Journal of the Acoustical Society of America, vol. 91, no. 1, pp.486-494. https://doi.org/10.1121/1.402734
  6. Hecht J.(2018), "Lidar for self-driving cars," Optics and Photonics News, vol. 29, no. 1, pp.26-33. https://doi.org/10.1364/opn.29.1.000026
  7. Hofmann U., Andre R. and Dickmanns E. D.(2003), "Radar and vision data fusion for hybrid adaptive cruise control on highways," Machine Vision and Applications, vol. 14, no. 1, pp.42-49. https://doi.org/10.1007/s00138-002-0093-y
  8. Hussein A. et al.(2018), "ROS and Unity Based Framework for Intelligent Vehicles Control and Simulation," 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp.1-6.
  9. Iqbal, A. et al.(2017), "Design of multifunctional autonomous car using ultrasonic and infrared sensors," 2017 International Symposium on Wireless Systems and Networks (ISWSN), IEEE.
  10. Jo J. et al.(2017), "A likelihood-based data fusion model for the integration of multiple sensor data: A case study with vision and lidar sensors," Robot Intelligence Technology and Applications 4, pp.489-500. https://doi.org/10.1007/978-3-319-31293-4_39
  11. Jo K. et al.(2014), "Development of autonomous car-Part I: Distributed system architecture and development process," IEEE Transactions on Industrial Electronics, vol. 61, p.12.
  12. KoROAD(2020), Statistical Analysis of Traffic Accidents, 2020 Edition, p.62.
  13. Kumar C. and Punitha R.(2020), "YOLOv3 and YOLOv4: Multiple Object Detection for Surveillance Applications," 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE.
  14. Li Q. et al.(2014), "A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios," IEEE Transactions on Vehicular Technology, vol. 63, no. 2, pp.540-555. https://doi.org/10.1109/TVT.2013.2281199
  15. Li Y. and Javier I. G.(2020), "Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems," IEEE Signal Processing Magazine, vol. 37, no. 4, pp.50-61. https://doi.org/10.1109/msp.2020.2973615
  16. Manjunath A. et al.(2018), "Radar based object detection and tracking for autonomous driving," 2018 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), IEEE.
  17. Okuda R., Yuki K. and Kazuaki T.(2014), "A survey of technical trend of ADAS and autonomous driving," Technical Papers of 2014 International Symposium on VLSI Design, Automation and Test, IEEE.
  18. Priemer C. and Friedrich B.(2009), "A decentralized adaptive traffic signal control using V2I communication data," 2009 12th International IEEE Conference on Intelligent Transportation Systems, pp.1-6.
  19. Rakha H. and Kamalanathsharma R. K.(2011), "Eco-driving at signalized intersections using V2I communication," 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp.341-346.
  20. Redmon J. and Ali F.(2018), "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767.
  21. Redmon J. et al.(2016), "You only look once: Unified, real-time object detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  22. Sualeh M. et al.(2019), "Dynamic multi-lidar based multiple object detection and tracking," 2019 Sensors, vol. 19, no. 1474, pp.1-20.
  23. Zhao F., Hao J. and Zongwei L.(2019), "Recent development of automotive LiDAR technology, industry and trends," Eleventh International Conference on Digital Image Processing (ICDIP 2019), vol. 11179, International Society for Optics and Photonics.
  24. Zhao G. W. and Shin'ichi Y.(1993), "Obstacle detection by vision system for an autonomous vehicle," 1993 Intelligent Vehicles Symposium, IV 1993, Institute of Electrical and Electronics Engineers Inc..
  25. Zhao X. et al.(2020), "Fusion of 3D LIDAR and camera data for object detection in autonomous vehicle applications," IEEE Sensors Journal, vol. 20, no. 9, pp.4901-4913. https://doi.org/10.1109/jsen.2020.2966034
  26. Zhi L. et al.(2018), "Navigation and Control System of Mobile Robot Based on ROS," 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp.368-372.
  27. Ziebinski A. et al.(2016), "A survey of ADAS technologies for the future perspective of sensor fusion," International Conference on Computational Collective Intelligence, Springer, Cham.