Multi-lane Detection Based on RMFP For Self-Driving in urban traffic scenes

DOI

10.2991/icsma-16.2016.121How to use a DOI?

Keywords

Multi-lane detection, Autonomous vehicle, RMFP, IPM, Kalman Filter

Abstract

The lane detection is important for the autonomous vehicle vision navigation used in the intelligent transportation system (ITS). Several approaches for lane detection were suggested in the past. However, there is still one issue about robustness. This paper presents a robust and real-time multi-lane detection method based on Road Marking Feature Points (RMFP) for autonomous vehicle navigation in urban environment. The key idea is to apply methods from extracting RMFP and the target tracking domain to identify lanes information in this article. Then we extract the RMFP from the gray-scale image and the IPM image. Besides we also use the lane line color and structure features to sift RMFP that meets lane line. At last, we adopt the clustering method to generate lane lines, and we track these lines by frame association and Kalman Filter. The experimental results show that our proposed method is robust and real-time detect the lane line of various kinds of complicated road. And based on the lane line visual navigation of unmanned experiment validate the reliability of our method.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chao Li
AU  - Hongzhe Liu
AU  - Yongrong Zheng
AU  - Hanyu Xuan
PY  - 2016/12
DA  - 2016/12
TI  - Multi-lane Detection Based on RMFP For Self-Driving in urban traffic scenes
BT  - Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016)
PB  - Atlantis Press
SP  - 692
EP  - 703
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsma-16.2016.121
DO  - 10.2991/icsma-16.2016.121
ID  - Li2016/12
ER  -