Map-based Lane and Obstacle-free Area Detection

T. Kowsari, S. S. Beauchemin, M. A. Bauer

2014

Abstract

With the emergence of intelligent Advanced Driving Assistance Systems (i-ADAS), the need for effective detection of vehicular surroundings is considered a necessity. The effectiveness of such systems directly depends on their performance in various environments such as rural and urban roads, and highways. Most of the current lane detection techniques are not suitable for urban roads with complex lane shapes and frequent occlusions. We propose a map-based lane detection approach which can robustly detect the lanes in urban and rural environments, and highways. We also present an algorithm for detecting obstacle-free areas in detected lanes based on the stereo depth maps of driving scenes. Experiments show that our approach reliably detects lanes and obstacle free areas within them, even in case of partially occluded or worn-off lane markers.

References

  1. Beauchemin, S., Bauer, M., Laurendeau, D., Kowsari, T., Cho, J., Hunter, M., and McCarthy, O. (2010). Roadlab: An in-vehicle laboratory for developing cognitive cars. In 23rd International Conference on Computer Applications in Industry and Engineering, pages 7- 12.
  2. Borkar, A., Hayes, M., Smith, M. T., and Pankanti, S. (2009). A layered approach to robust lane detection at night. In IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, pages 51- 57.
  3. Catmull, E. and Rom, R. (1974). A class of local interpolating splines. Computer-Aided Geometric Design, Academic Press, New York, pages 317-326.
  4. Cheng, H.-Y., Jeng, B.-S., Tseng, P.-T., and Fan, K.-C. (2006). Lane detection with moving vehicles in the traffic scenes. IEEE Transactions on Intelligent Transportation Systems, 7(4):571-582.
  5. Hillel, A. B., Lerner, R., Levi, D., and Raz, G. (2012). Recent progress in road and lane detection: a survey. Machine Vision and Applications, pages 1-19.
  6. Huang, A. S., Moore, D., Antone, M., Olson, E., and Teller, S. (2009). Finding multiple lanes in urban road networks with vision and lidar. Autonomous Robots, 26(2):103-122.
  7. Jiang, Y., Gao, F., and Xu, G. (2010). Computer visionbased multiple-lane detection on straight road and in a curve. In IEEE International Conference on Image Analysis and Signal Processing, pages 114-117.
  8. Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In IEEE International Conference on Neural Networks, volume 4, pages 1942-1948.
  9. Kim, Z. (2008). Robust lane detection and tracking in challenging scenarios. IEEE Transactions on Intelligent Transportation Systems, 9(1):16-26.
  10. McCall, J. C. and Trivedi, M. M. (2006). Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Transactions on Intelligent Transportation Systems, 7(1):20-37.
  11. Narita, Y., Katahara, S., and Aoki, M. (2003). Lateral position detection using side looking line sensor cameras. In Intelligent Vehicles Symposium, pages 271-275.
  12. Nieto, M., Salgado, L., Jaureguizar, F., and Arróspide, J. (2008). Robust multiple lane road modeling based on perspective analysis. In 15th IEEE International Conference on Image Processing, pages 2396-2399.
  13. Pomerleau, D. (1995). Ralph: Rapidly adapting lateral position handler. In Proc. Intelligent Vehicles Symposium, pages 506-511.
  14. Rasmussen, C. and Korah, T. (2005). On-vehicle and aerial texture analysis for vision-based desert road following. In IEEE Conference on Computer Vision and Pattern Recognition, pages 66-66.
  15. Samadzadegan, F., Sarafraz, A., and Tabibi, M. (2006). Automatic lane detection in image sequences for visionbased navigation purposes. In Proceedings of the ISPRS Commission, 5th Symposium on Image Engineering and Vision Metrology.
  16. Sawano, H. and Okada, M. (2006). A road extraction method by an active contour model with inertia and differential features. IEICE Transactions on Information and Systems, 89(7):2257-2267.
  17. Talbi, E.-G. and Muntean, T. (1993). Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem. In IEEE Proceedings of the 26th International Conference on System Sciences, volume 2, pages 565-573.
  18. Watt, A. and Watt, M. (1991). Advanced rendering and animation techniques: Theory and practice. Reading, MA.
  19. Wu, S.-J., Chiang, H.-H., Perng, J.-W., Chen, C.-J., Wu, B.- F., and Lee, T.-T. (2008). The heterogeneous systems integration design and implementation for lane keeping on a vehicle. IEEE Transactions on Intelligent Transportation Systems, 9(2):246-263.
  20. Zhou, Y., Hu, X., and Ye, Q. (2005). A robust lane detection approach based on map estimate and particle swarm optimization. In Computational Intelligence and Security, pages 804-811. Springer.
Download


Paper Citation


in Harvard Style

Kowsari T., S. Beauchemin S. and A. Bauer M. (2014). Map-based Lane and Obstacle-free Area Detection . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 523-530. DOI: 10.5220/0004675005230530


in Bibtex Style

@conference{visapp14,
author={T. Kowsari and S. S. Beauchemin and M. A. Bauer},
title={Map-based Lane and Obstacle-free Area Detection},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={523-530},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004675005230530},
isbn={978-989-758-009-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Map-based Lane and Obstacle-free Area Detection
SN - 978-989-758-009-3
AU - Kowsari T.
AU - S. Beauchemin S.
AU - A. Bauer M.
PY - 2014
SP - 523
EP - 530
DO - 10.5220/0004675005230530