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

Sung-ju Kim, Soon-Yong Park

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) or DGPS (Differential GPS) techniques are generally used in lane-level poisoning systems, which only provide an accuracy level up to 2-3 m. In this paper, we introduce a vision based lane-level positioning technique that provides more accurate prediction results. The proposed method predicts the current driving lane of the vehicle by tracking the 3D location of the traffic signs that are in the side-way of the road using a stereo camera. Several experiments are conducted to analyse the feasibility of the proposed method in driving lane level prediction. According to the experimental results, the proposed method could achieve 90.9% accuracy.

References

  1. Antkowiak, M. (2006). Artificial neural networks vs. support vector machines for skin diseases recognition. Master Degree, Department of Computing Science, Umea University, Sweden.
  2. Bahlmann, C., Zhu, Y., Ramesh, V., Pellkofer, M., and Koehler, T. (2005). A system for traffic sign detection, tracking, and recognition using color, shape, and motion information. In Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, pages 255-260. IEEE.
  3. Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2):121-167.
  4. Dao, T.-S., Leung, K. Y. K., Clark, C. M., and Huissoon, J. P. (2007). Markov-based lane positioning using intervehicle communication. Intelligent Transportation Systems, IEEE Transactions on, 8(4):641-650.
  5. De La Escalera, A., Moreno, L. E., Salichs, M. A., and Armingol, J. M. (1997). Road traffic sign detection and classification. Industrial Electronics, IEEE Transactions on, 44(6):848-859.
  6. Du, J. and Barth, M. J. (2008). Next-generation automated vehicle location systems: Positioning at the lane level. Intelligent Transportation Systems, IEEE Transactions on, 9(1):48-57.
  7. Du, J., Masters, J., and Barth, M. (2004). Lane-level positioning for in-vehicle navigation and automated vehicle location (avl) systems. In Intelligent Transportation Systems, 2004. Proceedings. The 7th International IEEE Conference on, pages 35-40. IEEE.
  8. García-Garrido, M. Í., Sotelo, M. Í., and MartínGorostiza, E. (2005). Fast road sign detection using hough transform for assisted driving of road vehicles. In Computer Aided Systems Theory-EUROCAST 2005, pages 543-548. Springer.
  9. Garcia-Garrido, M. A., Sotelo, M. A., and MartmGorostiza, E. (2006). Fast traffic sign detection and recognition under changing lighting conditions. In Intelligent Transportation Systems Conference, 2006. ITSC'06. IEEE, pages 811-816. IEEE.
  10. Kühnl, T., Kummert, F., and Fritsch, J. (2012). Spatial ray features for real-time ego-lane extraction. In Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on, pages 288-293. IEEE.
  11. Kuhnl, T., Kummert, F., and Fritsch, J. (2013). Visual ego-vehicle lane assignment using spatial ray features. In Intelligent Vehicles Symposium (IV), 2013 IEEE, pages 1101-1106. IEEE.
  12. Maldonado-Bascón, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gómez-Moreno, H., and L ópez-Ferreras, F. (2007). Road-sign detection and recognition based on support vector machines. Intelligent Transportation Systems, IEEE Transactions on, 8(2):264-278.
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Paper Citation


in Harvard Style

Kim S. and Park S. (2016). Lane-level Positioning based on 3D Tracking Path of Traffic Signs . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 642-648. DOI: 10.5220/0005721106420648


in Bibtex Style

@conference{visapp16,
author={Sung-ju Kim and Soon-Yong Park},
title={Lane-level Positioning based on 3D Tracking Path of Traffic Signs},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={642-648},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005721106420648},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Lane-level Positioning based on 3D Tracking Path of Traffic Signs
SN - 978-989-758-175-5
AU - Kim S.
AU - Park S.
PY - 2016
SP - 642
EP - 648
DO - 10.5220/0005721106420648