본문 바로가기
KERD
논문KCI 등재

교통신호 제어를 위한 라이다 기반 좌회전 감응 체계 개발

Development of Left Turn Response System Based on LiDAR for Traffic Signal Control

0

조회

0

다운로드

2

인용

0

참고문헌

키워드

주제분류컴퓨터학

초록

이 논문에서 우리는 기존의 영상식 또는 루프식 좌회전 감응 시스템과는 달리 라이다(LiDAR)센서및 영상 카메라를 이용하여, 좌회전 대기 차량을 2중으로 검지하고 좌회전 차선의 대기 길이에 대응하는좌회전 교통신호체계를 효율적으로 부여할 수 있는 시스템을 소개하였다. LiDAR 센서에 의해 송수신된LiDAR 신호에 대해서는 실시간으로 좌회전 대기 차량을 검지하고, 영상 카메라에 의한 영상에 대해서는실시간 또는 일정한 주기별로 분석하도록 구성함으로써 불필요한 연산 처리를 절감시켜 실시간 감응처리가 가능하도록 하였다. 실제 신호처리기를 이용한 교차로 시뮬레이션으로 1주일 동안 매일 5시간성능 테스트를 해 본 결과 기존 방식에 비해 3%~5% 향상된 99.9%의 검지율을 기록하였다. 장점으로는LiDAR 센서로 99.9% 좌회전 대기 차량이 검지되었으며 의도적인 검지 누락을 발생시키더라도, 영상에의한 자체 보정을 통해 즉각적인 대응이 가능하여 좌회전 대기 차량의 과도한 대기시간을 조절시켜교차로 내 모든 차선의 교통흐름을 원활하게 유도할 수 있었다. 또한 좌회전 차량이 뜸한 외곽의 교차로에 적용 시 불필요한 신호 비용을 줄이는 등 서비스 신뢰도 및 효율성을 높일 수 있다.

Abstract

In this paper, we use a LiDAR sensor and an image camera to detect a left-turning waiting vehicle in two ways, unlike the existing image-type or loop-type left-turn detection system, and a left-turn traffic signal corresponding to the waiting length of the left-turning lane. A system that can efficiently assign a system is introduced. For the LiDAR signal transmitted and received by the LiDAR sensor, the left-turn waiting vehicle is detected in real time, and the image by the video camera is analyzed in real time or at regular intervals, thereby reducing unnecessary computational processing and enabling real-time sensitive processing. As a result of performing a performance test for 5 hours every day for one week with an intersection simulation using an actual signal processor, a detection rate of 99.9%, which was improved by 3% to 5% compared to the existing method, was recorded. The advantage is that 99.9% of vehicles waiting to turn left are detected by the LiDAR sensor, and even if an intentional omission of detection occurs, an immediate response is possible through self-correction using the video, so the excessive waiting time of vehicles waiting to turn left is controlled by all lanes in the intersection. was able to guide the flow of traffic smoothly. In addition, when applied to an intersection in the outskirts of which left-turning vehicles are rare, service reliability and efficiency can be improved by reducing unnecessary signal costs.

참고문헌 (16)

  1. Robert J. Wang, "Pelee: A Real-Time Object Detection System on Mobile Devices", Advances in neural information processing system, pp.1963~1972 (2018)
  2. Tsung-Yi Lin, "Focal Loss for Dense Object Detection", Proceedings of the IEEE international conference on computer vision, pp.2980~2988 (2017)
  3. Joseph Redmon, "YOLOv3: An Incremental Improvement", ~ (2018)
  4. Mark Sandler, "Mobile NetV2: Inverted Residuals and Linear Bottlenecks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4510~4520 (2018)
  5. Yuan Wang, "PointSeg:Real-Time Semantic Segmentation Based on 3D Lidar Point Cloud", ~ (2018)
  6. Bin Yang, "PIXOR: Real-time 3D Object Detection from Point Clouds", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.7652~7660 (2019)
  7. Ming Liang, "Deep Continuous Fusion for Multi-Sensor 3D Object Detection", Proceedings of the European Conference on Computer Vision, pp.641~656 (2018)
  8. Xiaozhi Chen, "Multi-View 3D Object Detection Network for Autonomous Driving", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1907~1915 (2017)
  9. Pan Wei, "Lidar and Camera Detection Fusion in a Real-Time Industrial Multi-Sensor Collision Avoidance System", Electronics, 7(84) (2018)
  10. Wei Liu, "SSD:Single Shot MultiBox Detector", Proceedings of the European Conference on Computer Vision, pp.21~37 (2016)
  11. Ross Girshick, "Rich feature hierarchies for accurate object detection and semantic segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, pp.580~587 (2014)
  12. Ross Girshick, "Fast R-CNN", Proceedings of the IEEE international conference on computer vision, pp.1440~1448 (2015)
  13. Shaoqing Ren, "Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks", Advances in neural information processing system, pp.91~99 (2015)
  14. Liu Mingjie, "Online multiple object tracking using confidence score-based appearance model learning and hierarchical data association", IET Computer Vision, 13(3), pp.312~318 (2019)[DOI]
  15. Morgan Quigley, "ROS:an open-source Robot Operating System", ICRA workshop on open source software, 3(3), pp.5~ (2009)
  16. 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

관련 논문AI추천

이 간행물의 다른 논문 (34건)