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

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis Study of Mobile LiDAR Performance Degradation in Rainfall Based on Real-World Point Cloud Data

강우 시 모바일 LiDAR 성능저하에 대한 실측 점군데이터 기반 해석 연구

  • Youngmin Kim (Dept. of Road Transportation, Korea Institute of Civil Eng. and Building Tec. / Dept. of CEE, Seoul Nat'l Univ.) ;
  • Bumjin Park (Dept. of Road Transportation, Korea Institute of Civil Eng. and Building Tec.)
  • 김영민 (한국건설기술연구원 도로교통연구본부/서울대학교 건설환경공학부) ;
  • 박범진 (한국건설기술연구원 도로교통연구본부)
  • Received : 2024.09.11
  • Accepted : 2024.10.02
  • Published : 2024.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

LiDAR is a key sensor used in autonomous vehicles, and its range of applications is expanding because it can generate 3D information and is relatively robust to various environmental factors. However, it is known that LiDAR performance is degraded to some extent due to signal attenuation and scattering by raindrops during rain, and thus the need for analysis of factors affecting rainfall in road environment detection and utilization using LiDAR has been confirmed. In this study, we analyze how signal attenuation and scattering, known as factors degrading LiDAR performance during rain, cause performance degradation based on real data. We acquire data using facilities that utilize high-luminosity retroreflective sheeting in indoor chamber where quantity of rainfall can be controlled, and quantitatively confirm the degradation of LiDAR performance during rain by interpreting it from the perspective of signal attenuation and scattering. According to the point cloud distribution and performance analysis results, LiDAR performance deteriorates due to signal attenuation and scattering caused by rain. Specifically, the quantitative performance analysis shows that LiDAR experiences a decrease in intensity primarily due to signal attenuation from rain, as well as a reduction in NPC and intensity due to signal scattering effects, along with an increase in measurement distance error.

LiDAR는 자율주행차에서 활용되는 핵심적인 센서로서, 3D 정보를 생성할 수 있고 다양한 환경요인에 비교적 강건하다는 점에서 활용 범위가 늘어나고 있다. 다만 LiDAR는 원리 상 강우 시 빗방울에 의한 신호감쇠 및 산란현상으로 인해 성능이 일정 부분 저하되는 것으로 알려져 있으며, 이에 LiDAR를 활용한 도로환경 검지 및 활용에 있어 강우에 따른 영향요인 분석이 필요하다. 본 연구에서는 강우 시 LiDAR의 성능 저하 요인이라 알려진 신호감쇠 및 산란의 영향에 대한 해석을 실측 데이터를 기반하여 진행한다. 강우량 통제가 가능한 실내 실험시설에서 고휘도 재귀반사시트지를 활용한 시설물을 활용하여 데이터를 취득하고, 이를 신호감쇠 및 산란의 관점에서 해석하여 강우 시 LiDAR 성능 저하를 정량적으로 확인한다. 점군분포도와 성능지표 분석결과, 비로 인한 신호감쇠와 산란의 영향으로 LiDAR의 성능은 저하된다. 세부적으로 정량 성능지표 분석결과는 LiDAR는 비로 인한 신호감쇠 효과로 주로 Intensity가 감소하며, 신호산란 효과로 NPC와 Intensity가 감소하고, 측정거리 오차가 커졌다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 21AMDP-C161924-01, 주관연구기관 과제명: 크라우드 소싱 기반의 디지털 도로교통 인프라 융합플랫폼 기술 개발 / 공동연구기관 과제명: 도로·교통 인프라 성능평가 방법론 개발 및 자율차 기반의 개발 인프라 검증)

References

  1. Beraldin, J. and Blais, F.(2010), Laser scanning technology: In Airborne and Terrestrial Laser Scanning, Whittles Publishing, pp.1-42. 
  2. Goodin, C., Carruth, D., Doude, M. and Hudson, C.(2019), "Predicting the influence of rain on LiDAR in ADAS", Electronics, vol. 8, no. 1, 89. doi: 10.3390/electronics8010089 
  3. Heinzler, R., Schindler, P., Seekircher, J., Ritter, W. and Stork, W.(2019), "Weather influence and classification with automotive lidar sensors", In 2019 IEEE Intelligent Vehicles Symposium (IV), pp.1527-1534. https://doi.org/10.1109/IVS.2019.8814205 
  4. How Google's self-driving car works - IEEE Spectrum, https://spectrum.ieee.org/how-google-self-driving-car-works, 2024.05.14. 
  5. ISO/TC 22/SC 32(2014), Test method for automotive LiDAR, International Organization for Standardization, London, UK. 
  6. Kim, J. and Kim, J.(2023), "An empirical study on development of traffic safety facilities for safe autonomous vehicle operation in construction areas", Journal of Korea Institute Intelligent Transportation System, vol. 22, no. 5, pp.163-281. 
  7. Kim, J. and Park, B. A.(2022), "Study of LiDAR's detection performance degradation in fog and rain climate", Journal of the Korea Institute of Intelligent Transportation Systems, vol. 21, no. 2, pp.101-115. https://doi.org/10.12815/kits.2022.21.2.101 
  8. Kim, J., Park, B. and Kim, J.(2023), "Empirical analysis of autonomous vehicle's LiDAR detection performance degradation for actual road driving in rain and fog", Sensors, vol. 23, no. 6, 2972. doi: 10.3390/s23062972 
  9. Kim, J., Park, B., Roh, C. and Kim, Y.(2021), "Performance of mobile LiDAR in the real road driving conditions", Sensors, vol. 21, no. 22, 7461. doi: 10.3390/s2201010 
  10. Korea Institute of Civil Engineering and Building Technology(KICT)(2021), Improved road infrastructures to strengthen driving safety of automated driving car final report. 
  11. Lambert, J., Carballo, A., Cano, A., Narksri, P., Wong, D., Takeuchi, E. and Takeda, K.(2020), "Performance analysis of 10 models of 3D LiDARs for automated driving", IEEE Access, vol. 8, pp.131699-131722. doi: 10.1109/ACCESS.2020.3009680 
  12. Li, Y. and Ibanez-Guzman, J.(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. 
  13. Montalban, K., Reymann, C., Atchuthan, D., Dupouy, P. E., Riviere, N. and Lacroix, S.(2021) "A quantitative analysis of point clouds from automotive lidars exposed to artificial rain and fog", Atmosphere, vol. 12, no. 6, 738. https://doi.org/10.3390/atmos12060738 
  14. OxTS, https://www.oxts.com/ko/products/oxts-technology-partner-velodyne-lidar, 2024.08.20. 
  15. Park, J., Cho, J., Lee, S., Bak, S. and Kim, Y.(2023) "An automotive LiDAR performance test method in dynamic driving conditions", Sensors, vol. 23, no. 8, 3892. doi; 10.3390/s23083892 
  16. Tang, L., Shi, Y., He, Q., Sadek, A. W. and Qiao, C.(2020), "Performance test of autonomous vehicle LiDAR sensors under different weather conditions", Transportation Research Record, vol. 2674, no. 1, pp.319-329. 
  17. UL4700(2021), Standard for safety for LiDAR and LiDAR systems, Underwriters Laboratory (UL): Northbrook, IL, USA.
  18. wikipedia, https://ko.wikipedia.org/wiki/DARPA_그랜드_챌린지 2024.08.20.