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 Study of LiDAR's Detection Performance Degradation in Fog and Rain Climate

안개 및 강우 상황에서의 LiDAR 검지 성능 변화에 대한 연구

  • Kim, Ji yoon (Dept. of Highway & Transportation Research, KICT) ;
  • Park, Bum jin (Dept. of Highway & Transportation Research, KICT)
  • 김지윤 (한국건설기술연구원 도로교통연구본부) ;
  • 박범진 (한국건설기술연구원 도로교통연구본부)
  • Received : 2022.02.17
  • Accepted : 2022.03.22
  • Published : 2022.04.30
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Abstract

This study compared the performance of LiDAR in detecting objects in rough weather with that in clear weather. An experiment that reproduced rough weather divided the fog visibility into four stages from 200 m to 50 m and controlled the rainfall by dividing it into 20 mm/h and 50 mm/h. The number of points cloud and intensity were used as the performance indicators. The difference in performance was statistically investigated by a T-Test. The result of the study indicates that the performance of LiDAR decreased in the order in situations of 20 mm/h rainfall, fog visibility less than 200 m, 50 mm/h rainfall, fog visibility less than 150 m, fog visibility less than 100 m, and fog visibility less than 50 m. The decreased performance was greater when the measurement distance was greater and when the color was black rather than white. However, in the case of white, there was no difference in performance at a measurement distance of 10 m even at 50 m fog visibility, which is considered the worst situation in this experiment. This no difference in performance was also statistically significant. These performance verification results are expected to be utilized in the manufacture of road facilities in the future that improve the visibility of sensors.

본 연구는 LiDAR가 악천후 시 물체를 검지하는 성능을 맑은 날과 비교하여 알아보았다. 악천후를 재현하는 실험은 안개 시정거리를 200m부터 50m까지 4단계로 강우량은 20(mm/h)와 50(mm/h)로 나누어 수행하였다. LiDAR를 차량에 장착하여 실제 도로 위를 주행하여 진행하였고, 사람 모양의 표지판을 대상으로 측정거리별로 분석하였다. 성능지표는 Number of Points Cloud와 Intensity를 활용하였고, T-Test로 성능의 차이를 통계적으로 알아보았다. 연구결과, 맑은 날 대비 LiDAR 검지 성능은 강우량 20mm/h, 안개시정 200m 이하, 강우량 50mm/h, 안개시정 150m 이하, 100m 이하, 50m 이하의 순으로 성능저하가 발생하였다. 성능의 저하는 흰색보다는 검은색일 때, 측정거리가 멀어질수록 크게 발생하였다. 하지만, 흰색은 본 실험에서 최악의 상황으로 판단되는 시정 50m에서도 측정거리 10m에서는 성능의 차이가 미미하였고, 통계적으로는 차이가 없었다. 성능검증 결과는 향후 센서의 시인성을 제고하는 도로시설물 제작에 활용될 것이 기대된다.

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. A., Francois, B. and Uwe, L.(2010), "Laser Scanning technology", In Vosselman, G. and Mass, H. G. eds. Airborn and terrestrial laser scanning, Caithness, Whittles Publishing, pp.1-42.
  2. Bloomberg, https://www.bloomberg.com/news/articles/2018-09-17/self-driving-cars-still-can-t-handle-bad-weather, 2022.02.17.
  3. Goodin, C., Carruth, D., Doude, M. and Hudson, C.(2019), "Predicting the Influence of Rain on LiDAR in ADAS", Electronics, vol. 8, no. 1, p.89, doi: 10.3390/electronics8010089
  4. Guan, H., Li, J. and Yu, Y.(2016), "Use of mobile LiDAR in Road information inventory: A review", International Journal of Image and Data Fusion, vol. 7, no. 3, pp.219-242. https://doi.org/10.1080/19479832.2016.1188860
  5. Herrmann, A., Brenner, W. and Stodler, R.(2019), Autonomous Driving: How to driverless Revolution Will Change the World, Emerald Publishing, pp.113-128.
  6. Jeon, H. and Kim, J.(2021), "Analysis on Handicaps of Automated Vehicle and Their Causes using IPA and FGI", Journal of Korea Institute Intelligent Transportation System, vol. 20, no. 3, pp.34-46. https://doi.org/10.12815/kits.2021.20.3.34
  7. 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, p.7461, doi: 10.3390/s22010106
  8. Korea Institute Construction and Technology(2021), Improved Road Infrastructures to Strengthen Driving Safety of Automated Driving Car, pp.39-55.
  9. Korea Transport Safety Authority, https://www.kotsa.or.kr, 2022.02.16.
  10. Kutila, M., Pyykonen, P., Ritter, W., Sawade, O. and Schaufele, B.(2016), "Automative LiDAR Sensor Development Scenarios for Harsh Weather Conditions", IEEE 19th International Conference on Intelligent Transportation Systems(ITSC), Rio De Janeiro, Brazil, IEEE, Newyork, pp.265-270.
  11. Lee, H. and Suh, M.(2019), "Objective Classification of Fog Type and Analysis of Fog Characteristics Using Visibility Meter and Satellite Observation Data over South Korea", Atmosphere, vol. 29, no. 5, pp.639-658. https://doi.org/10.14191/Atmos.2019.29.5.639
  12. Lee, S., Park, W., Jin, M. and Kim, Y.(2020), "Relationship Between Pavement Marking Types and Visibility Distance Under Adverse Weather Conditions", International Journal of Highway Engineering, vol. 22, no. 5, pp.85-93. https://doi.org/10.7855/ijhe.2020.22.5.085
  13. 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. https://doi.org/10.1109/msp.2020.2973615
  14. 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, p.738, doi: 10.3390/atmos12060738
  15. Park, B. and Kim, J.(2021), "A Study of LiDAR's Performance Change by Road Sign's Color and Climate", Journal of Korea Institute Intelligent Transportation System, vol. 20, no. 6, pp.228-241. https://doi.org/10.12815/kits.2021.20.6.228
  16. Roh, C. G. and Im, I.(2020), "A review on handicap sections and situations to improve driving safety of automated vehicles", Sustainability, vol. 12, no. 14, p.5509, doi: 10.3390/su12145509
  17. 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. https://doi.org/10.1177/0361198120901681
  18. YTN, https://www.ytn.co.kr/_ln/0108_201607051237366868, 2022.02.17.