LIDAR 센서 검지기반 차량 입출차 정확성과 신뢰도 향상을 위한 주차제어시스템 개발
Development of a parking control system that improves the accuracy and reliability of vehicle entry and exit based on LIDAR sensing detection
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이 논문에서 우리는 제4차 산업혁명의 핵심기술의 하나인 LiDAR 센서를 기반으로 기존 검지 카메라의 검지율을 개선하여 입출차 차량에 대해 100% 검지가능한 시스템을 개발하였다. 현재 운영 중인주차장은 98% 정도의 차량번호 인식율에만 의존하고 있으므로 입출차 카운트의 불일치, 부정확한 정보 제공 등으로 사전 예약불가, 실시간 주차정보 불일치 등 여러 가지 문제를 안고 있다. 주차현황정보는 정확도 100% 수준으로 관리되어야 하며 이를 위해 우리는 LIDAR를 이용하여 주차장의 입출차검지 체계를 구축하였다. 주로 자율주행 자동차의 차량 및 사물검지를 위해 필수적으로 사용되고 있는LIDAR 센서를 응용하여 주차시스템을 개발하는 경우, 검지된 센싱 정보로 차량 입출차 정보의 정확성과 입출차 카운트의 신뢰도를 개선할 수 있다. LIDAR의 분해능은 100%로 보장이 되었고 주차장의입차(+), 출차(-) 차량의 합계가 0이 되도록 구현할 수 있었다. 우리는 3,000대의 실제 주차장 출입 차량으로 테스트해 본 결과 주차 차량 입출차 정확도를 100%로 결과를 도출하였다.
Abstract
In this paper, we developed a 100% detection system for entering and leaving vehicles by improving the detection rate of existing detection cameras based on the LiDAR sensor, which is one of the core technologies of the 4th industrial revolution. Since the currently operating parking lot depends only on the recognition rate of the license plate number of about 98%, there are various problems such as inconsistency in the entry/exit count, inability to make a reservation in advance due to inaccurate information provision, and inconsistency in real-time parking information. Parking status information should be managed with 100% accuracy, and for this, we built a parking lot entrance/exit detection system using LIDAR. When a parking system is developed by applying the LIDAR sensor, which is mainly used to detect vehicles and objects in autonomous vehicles, it is possible to improve the accuracy of vehicle entry/exit information and the reliability of the entry/exit count with the detected sensing information. The resolution of LIDAR was guaranteed to be 100%, and it was possible to implement so that the sum of entering (+) and exiting (-) vehicles in the parking lot was 0. As a result of testing with 3,000 actual parking lot entrances and exits, the accuracy of entering and exiting parking vehicles was 100%.
참고문헌 (15)
- M. Mambo, "Proxy Signature :Delegation of the Power to Sign Messages", IEICE Trans. Fundamentals, E79-A(9), pp.1338~1353 (1996)
- "Reporter Jongseon Lee", Incheon Today, ~ (2020)
- "Domestic Registered Patent No. 10-1641326 No. 2090203", ~
- Hyungi Kim, "Estimate Poisition of Multiple Objects based on 3D LiDAR and Camera Sensor fusion", Transactions of KSAE, 5(3), pp.116~129 (2019)
- Taehyeong Kim, "Multiple Lidars Calibration and Deep Learning Based Vehicle Recognition", Transactions of KSAE, 4(2), pp.223~240 (2017)
- Juhyeok Ra, "A Study on Pedestrian and Vehicle Detection Algorithm by Integration of Camera and LIDAR Coordinate System", Transactions of KSAE, 6(2), pp.1157~1172 (2017)
- Xiao Zhang, "Efficient L-shape Fitting for Vehicle Detection Using Laser Scanners", IEEE Intelligent Vehicles Symposium, 11(9), pp.1~6 (2017)
- B. Wu, "SqueezeSegV2:Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud", ICRA, 6(3), pp.1721~1737 (2019)
- B. Wu, "Squeezeseg:Convolutional neural nets with recurrent crf for real-time Road-Object Segmentation from 3d LiDAR Point Cloud", ICRA, 3(3), pp.105~119 (2018)
- O. Vinyals, "Show and Tell: A Neural image caption generator", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), 13(17), pp.2156~2164 (2015)
- K. Xu, "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention", Proceedings of The 32th International Conference on Machine Learning, 12(5), pp.105~120 (2015)
- N. Srivastava, "Unsupervised Learning of Video Representation using LSTMs", Proceedings of The 32th International Conference on Machine Learning, 2(2), pp.11~19 (2019)
- L. Yao, "Video description generation incorporating spatio-temporal features and a soft-attention mechanism", 201~211 (2015)
- S. Venugopalan, "Sequence to Sequence-Video to Text", 1121~1138 (2017)
- J. -W. Ha, "Automated Construction of Visual-Linguistic Knowledge via Concept Learning from Cartoon Videos", Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI 2015), 11(3), pp.522~528 (2015)