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

A Study of LiDAR's Performance Change by Road Sign's Color and Climate

도로시설물의 색깔 및 기상 환경에 따른 LiDAR의 성능변화 연구

  • Park, Bum jin (Dept. of Highway & Transportation Research, KICT) ;
  • Kim, Ji yoon (Dept. of Highway & Transportation Research, KICT)
  • 박범진 (한국건설기술연구원 도로교통연구본부) ;
  • 김지윤 (한국건설기술연구원 도로교통연구본부)
  • Received : 2021.11.09
  • Accepted : 2021.11.18
  • Published : 2021.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study verified the performance change of a LiDAR when it detects road signs, which are potential cooperation targets for an autonomous vehicle. In particular, road signs of different colors and materials were produced and tested in controlled rainfall on the real road environment. The NPC and intensity were selected as the performance indicators, and a T-Test was used for comparison. The study results show that the performance of LiDAR for the detection of road signs was reduced with the increase of rainfall. The degradation of performance in retroreflective sheets was lesser than painted road signs, but at the amount of 40 mm/h or more, the detection performance of retroreflective sheets deteriorates to an extent that data cannot be collected. The performance level of black paint was lower than that of other colors on a clear day. In addition, the white sheet was most sensitively degraded with the increase in precipitation. These performance verification results are expected to be utilized in the manufacturing of road facilities that improve the visibility of sensors in the future.

본 연구는 자율협력주행차량의 협력 인프라 중 하나인 도로표지판을 대상으로 LiDAR의 검지성능 변화를 알아보았다. 이를 위해서 색깔과 재질이 다른 도로표지판을 제작하여 실제도로 환경에서 강우량을 통제한 테스트를 수행하였다. 성능지표는 NPC와 Intensity로 선정하였고, 집단간의 비교는 T-Test를 활용하였다. 연구결과, 모든 재질에서 강수량이 증가할수록 LiDAR의 성능지표가 감소되는 결과가 관측되었다. 재귀반사지는 강수량 증가에 따른 성능지표 감소가 페인트 도색에 비해선 작았지만, 이 역시 40mm이상의 강수량에서는 데이터의 관측이 되지 않을 정도로 성능이 저하되었다. 검은색 페인트는 맑은 날에도 다른 색들에 비하여 성능지표가 낮았으며 특히, 백색의 재귀반사지는 성능지표가 강수량 증가에 가장 민감하게 저하되었다. 이러한 성능검증 결과는 향후 센서의 시인성을 제고하는 도로시설물 제작에 활용될 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 21AMDP-C161924-01, 크라우드 소싱기반의 디지털 도로교통 인프라 융합플랫폼 기술 개발)

References

  1. Beraldin J. A., Francois B. and Uwe L.(2010), "Laser Scanning technology," In G. Vosselman and H. Mass, eds. Airborn and terrestrial laser scanning, Caithness, Whittles Publishing, pp.1-42.
  2. Chan E.(2021), LiDAR vs. Camera0The Best Sensor Suite, Mirae Asset Global Investments (Hong Kong), https://www.am.miraeasset.com.hk/insight/lidar-vs-camera-only-what-is-the-best-sensor-suite-combination- for-full-autonomous-driving/
  3. Chen C., Fragonara L. Z. and Tsourdos A.(2021), "RoIFusion: 3D Object Detection From LiDAR and Vision," IEEE Access, vol. 9, pp.51710-51721. https://doi.org/10.1109/ACCESS.2021.3070379
  4. Dannheim C., Icking C., Mader M. and Sallis P.(2014), "Weather Detection in Vehicles by means of Camera and LiDAR systems," 6th International Conference on Computational Intelligence, Communication Systems and Networks, doi: 10.1109/CICSyN.2014.47
  5. Goberville N., El-Yabroudi M., Omwanas M., Rojas J., Meyer R., Asher Z. and Abdel-Qader I.(2020), "Analysis of LiDAR and Camera Data in Real-World Weather Conditions for Autonomous Vehicle Operations," SAE Technical Paper, doi: 10.4271/2020-01-0093
  6. 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
  7. Guan H., Li J., Cao S. 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
  8. Heinzler R., Schindler P., Seekircher J., Ritter W. and Stork W.(2019), "Weather Influence and Classification with Automotive Lidar Sensors," IEEE Intelligent Vehicles Symposium(IV), Paris, France, June 9-12.
  9. Jeon H. M. and Kim J. S.(2021), "Analysis on Handicaps of Automated Vehicle and Their Causes using IPA and FGI," Journal of Korea Institute of Intelligent Transport System, vol. 20, no. 3, pp.34-46. https://doi.org/10.12815/kits.2021.20.3.34
  10. Kim W. K.(2020), "Main Contents and Future Plans of the Automated Driving Technology Development Innovation Project," Monthly KOTI Magazine on Transportation, vol. 272, pp.27-35.
  11. Korea Institute Construction and Technology(2021), Improved Road Infrastructures to Strengthen Driving Safety of Automated Driving Car 2th Report.
  12. Kutila M., Pyykonen P., Ritter W., Sawade O. and Schaufele B.(2016), "Automotive LIDAR Sensor Development Scenarios for Harsh Weather Conditions," Proc., 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, IEEE, New York, pp.265-270.
  13. Lavarone A.(2007), "Feature: Terrestrial LiDAR goes mobile," Professional Surveyor Magazine.
  14. Lee I. S. and Lee J. O.(2010), "Performance evaluation of Terrestrial Laser Scanner over Calibration Baseline," Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, vol. 28, no. 3, pp.329-336.
  15. 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
  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, 5509. doi: 10.3390/su12145509
  17. Roh C. G., Kim J. and Im I.(2020), "Analysis of Impact of Rain Conditions on ADAS," Sensors, vol. 20, 6720, doi: 10.3390/s20236720
  18. Stock K.(2018. 09. 17), Self-Driving Cars Can Handle Neither Rain nor Sleet nor Snow, Bloomberg Businessweek.
  19. 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
  20. Vargas Rivero J. R., Gerbich T., Teiluf V., Buschardt B. and Chen J.(2020), "Weather Classification Using an Automotive LIDAR Sensor Based on Detections on Asphalt and Atmosphere," Sensors, vol. 20, 4306. doi: 10.3390/s20154306.