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
- Beraldin, J. and Blais, F.(2010), Laser scanning technology: In Airborne and Terrestrial Laser Scanning, Whittles Publishing, pp.1-42.
- 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
- 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
- GSA Global(n.d.), Autonomous driving and sensor fusion SoCs, Available online: https://www.gsaglobal.org/forums/autonomous-driving-and-sensor-fusion-socs/, 2022.10.05.
- 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
- Kim, J. and Park, B.(2022a), "A Research of Factors Affecting LiDAR's Detection on Road Signs: Focus on Shape and Height of Road Sign", Journal of Korea Institute Intelligent Transportation System, vol. 21, no. 4, pp.34-46. https://doi.org/10.12815/kits.2022.21.4.190
- Kim, J. and Park, B.(2022b), "A Study of LiDAR's Detection Performance Degradation in Fog and Rain Climate", Journal of Korea Institute Intelligent Transportation System, vol. 21, no. 2, pp.101-115. https://doi.org/10.12815/kits.2022.21.2.101
- 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, p.2972. doi: 10.3390/s23062972
- 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/s2201010
- Kim, S., Ha, J. and Jo, K.(2021), "Semantic Point Cloud-Based Adaptive Multiple Object Detection and Tracking for Autonomous Vehicles", IEEE Access, vol. 9, pp.157550-157562. doi: 10.1109/ACCESS.2021.3130257
- Korea Institute of Civil Engineering and Building Technology(KICT)(2021), Improved Road Infrastructures to Strengthen Driving Safety of Automated Driving Car Final Report.
- Kutila, M., Pyykonen, P., Ritter, W., Sawade, O. and Schaufele, B.(2016), "Automotive LIDAR sensor development scenarios for harsh weather conditions", Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 1-4 November 2016, IEEE, New York, NY, USA, 2016, pp.265-270.
- 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
- 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
- Park, B.(2022), "Method of improvements for autonomous vehicle road-traffic facilities using LiDAR", The Korea Institute of Intelligent Transportation Systems(KITS) International Conference Special Session B-5.
- 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
- Yan, X., Gao, J., Zheng, C., Zheng, C., Zhang, R., Cui, S. and Li, Z.(2022), "2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds", Computer Vision, ECCV 2022-17th European Conference, Part XXVIII, pp.677-695. doi: 10.1007/978-3-031-19815-1_39
- Zamanakos, G., Tsochatzidis, L., Amanatiadis, A. and Pratikakis, I.(2021), "A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving", Computers & Graphics, vol. 99, pp.153-181. doi: 10.1016/j.cag.2021.07.003