DOI QR코드

DOI QR Code

Development of Color Recognition Algorithm for Traffic Lights using Deep Learning Data

딥러닝 데이터 활용한 신호등 색 인식 알고리즘 개발

  • 백서하 (서울대학교 공과대학 기계공학부) ;
  • 김종호 (서울대학교 공과대학 기계공학부) ;
  • 이경수 (서울대학교 공과대학 기계공학부)
  • Received : 2021.05.03
  • Accepted : 2022.01.26
  • Published : 2022.06.30

Abstract

The vehicle motion in urban environment is determined by surrounding traffic flow, which cause understanding the flow to be a factor that dominantly affects the motion planning of the vehicle. The traffic flow in this urban environment is accessed using various urban infrastructure information. This paper represents a color recognition algorithm for traffic lights to perceive traffic condition which is a main information among various urban infrastructure information. Deep learning based vision open source realizes positions of traffic lights around the host vehicle. The data are processed to input data based on whether it exists on the route of ego vehicle. The colors of traffic lights are estimated through pixel values from the camera image. The proposed algorithm is validated in intersection situations with traffic lights on the test track. The results show that the proposed algorithm guarantees precise recognition on traffic lights associated with the ego vehicle path in urban intersection scenarios.

Keywords

Acknowledgement

본 연구는 국토교통부 도심도로 자율협력주행 안전·인프라 연구사업의 연구비지원(과제번호 19PQOW-B152473-01)에 의해 수행되었습니다.

References

  1. Banks, Victoria A., Katherine L. Plant, and Neville A. Stanton, "Driver error or designer error: Using the Perceptual Cycle Model to explore the circumstances surrounding the fatal Tesla crash on 7th May 2016", Safety science 108 (2018): 278~285. https://doi.org/10.1016/j.ssci.2017.12.023
  2. Lee, Hojoon, et al., "Moving Object Detection and Tracking Based on Interaction of Static Obstacle Map and Geometric Model-Free Approach for Urban Autonomous Driving", IEEE Transactions on Intelligent Transportation Systems (2020).
  3. Song, Huansheng, et al., "Vision-based vehicle detection and counting system using deep learning in highway scenes", European Transport Research Review 11.1 (2019): 1~16. https://doi.org/10.1186/s12544-018-0328-2
  4. Deng, Ruoqi, Boya Di, and Lingyang Song, "Cooperative collision avoidance for overtaking maneuvers in cellular V2X-based autonomous driving", IEEE Transactions on Vehicular Technology 68.5 (2019): 4434~4446. https://doi.org/10.1109/tvt.2019.2906509
  5. Sun, Liting, et al., "Behavior planning of autonomous cars with social perception", 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2019.
  6. Hobert, Laurens, et al., "Enhancements of V2X communication in support of cooperative autonomous driving", IEEE communications magazine 53.12 (2015): 64~70. https://doi.org/10.1109/MCOM.2015.7355568
  7. Redmon, Joseph, et al., "You only look once: Unified, real-time object detection", Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.