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Lane-Level Positioning based on 3D Tracking Path of Traffic Signs

교통 표지판의 3차원 추적 경로를 이용한 자동차의 주행 차로 추정

  • Received : 2016.04.22
  • Accepted : 2016.05.31
  • Published : 2016.08.31

Abstract

Lane-level vehicle positioning is an important task for enhancing the accuracy of in-vehicle navigation systems and the safety of autonomous vehicles. GPS (Global Positioning System) and DGPS (Differential GPS) are generally used in navigation service systems, which however only provide an accuracy level up to 2~3 m. In this paper, we propose a 3D vision based lane-level positioning technique which can provides accurate vehicle position. The proposed method determines the current driving lane of a vehicle by tracking the 3D position of traffic signs which stand at the side of the road. Using a stereo camera, the 3D tracking paths of traffic signs are computed and their projections to the 2D road plane are used to determine the distance from the vehicle to the signs. Several experiments are performed to analyze the feasibility of the proposed method in many real roads. According to the experimental results, the proposed method can achieve 90.9% accuracy in lane-level positioning.

Keywords

References

  1. https://www.youtube.com/watch?v=j2rq_8yV0p8, CES 2016, nVidia Drive CX, Digital Dashboard and Infotainment System, 2016.
  2. M. Han, K. Lee, S. Jo, "Robust Lane Detection Algorithm for Realtime Control of an Autonomous Car," Journal of Korea Robotics Society, vol. 6, no. 2, pp. 165-172, 2011. https://doi.org/10.7746/jkros.2011.6.2.165
  3. A. De La Escalera, L. E. Moreno, M. A. Salichs, and J. M. Armingol, "Road traffic sign detection and classification," Industrial Electronics, IEEE Transactions, vol. 44, no. 6, pp. 848-859, 1997. https://doi.org/10.1109/41.649946
  4. M.A. Garcia-Garrido, M.A. Sotelo, and E. Martm-Gorostiza, "Fast traffic sign detection and recognition under changing lighting conditions," Intelligent Transportation Systems Conference, IEEE, 2006.
  5. S. Maldonado-Bascon, S. Lafuente-Arroyo and F. Gil-Jimenez, "Road-sign detection and recognition based on support vector machines," Intelligent Transportation Systems, IEEE Transactions, vol. 8, no. 2, pp. 264-278, 2007. https://doi.org/10.1109/TITS.2007.895311
  6. L. Chen, Q. M. Li, and Q. Mao, "Traffic sign detection and recognition for intelligent vehicle," Intelligent Vehicles Symposium (IV), IEEE, 2011.
  7. J. Du, J. Masters and M. Barth, "Lane-level positioning for in-vehicle navigation and automated vehicle location (AVL) systems," Intelligent Transportation Systems, Proceedings, The 7th International IEEE Conference, 2004.
  8. N. Alam, A.T. Balaei, and A.G. Dempster "An instantaneous lane-level positioning using DSRC carrier frequency offset," Intelligent Transportation Systems, IEEE Transactions, vol. 13, no. 4, pp. 1566-1575, 2012. https://doi.org/10.1109/TITS.2012.2195177
  9. J. Du, "Next-generation automated vehicle location systems: Positioning at the lane level," Intelligent Transportation Systems, IEEE Transactions on, vol. 9, no. 1, pp. 48-57, 2008. https://doi.org/10.1109/TITS.2007.908141
  10. T.S. Dao, K.Y. Leung, C.M. Clark and J.P. Huissoon, "Markov-based lane positioning using inter-vehicle communication," Intelligent Transportation Systems, IEEE Transactions, vol. 8, no. 4, pp. 641-650, 2007. https://doi.org/10.1109/TITS.2007.908574
  11. T. Kuhnl, F. Kummert and J. Fritsch, "Spatial ray features for real-time ego-lane extraction," Intelligent Transportation Systems 15th International IEEE Conference, 2012.
  12. T. Kuhnl, F. Kummert, and J. Fritsch, "Visual egovehicle lane assignment using spatial ray features," Intelligent Vehicles Symposium, IEEE, 2013.
  13. M.A. Garcia-Garrido, M.A. Sotelo, and E. Martin-Gorostiza, "Fast road sign detection using Hough transform for assisted driving of road vehicles," Computer Aided Systems Theory--EUROCAST, Springer, 2005, pp. 543-548.
  14. C. Bahlmann, Y. Zhu, V. Ramesh, M. Pellkofer, and T. Koehler, "A system for traffic sign detection, tracking, and recognition using color, shape, and motion information," Intelligent Vehicles Symposium, IEEE, 2005.
  15. J. Stallkamp, M. Schlipsing, J. Salmen and C. Igel, "Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition," Neural networks, vol. 32, pp. 323-332, 2012. https://doi.org/10.1016/j.neunet.2012.02.016
  16. D. Ciresan, u. Meier, J. Masci, and J. Schmidhuber, "Multi-column deep neural network for traffic sign classification," Neural Networks, vol. 32, pp. 333-338, 2012. https://doi.org/10.1016/j.neunet.2012.02.023
  17. J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, "The German traffic sign recognition benchmark: a multi-class classification competition," Neural Networks (IJCNN), The 2011 International Joint Conference, 2011.
  18. P. Dollar, Z. Tu, P. Perona and S. Belongie, "Integral Channel Features," British Machine Vision Conference, vol. 2, no. 3, p. 5, 2009.
  19. C. M. Bishop, Pattern recognition and machine learning, Springer, 2006.
  20. J.H. Lee, J. Lee, A. Lee, J. Kim, "Geometric Formulation of Rectangle Based Relative Localization of Mobile Robot," Journal of Korea Robotics Society, vol. 11, no. 1, pp.9-18, 2016. https://doi.org/10.7746/jkros.2016.11.1.009

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