Citation

Danescu R, Nedevschi S. IEEE Trans. Intel. Transp. Syst. 2009; 10(2): 272-282.

Copyright

(Copyright © 2009, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2009.2018328

PMID

unavailable

Abstract

Accurate and robust lane results are of great significance in any driving-assistance system. To achieve robustness and accuracy in difficult scenarios, probabilistic estimation techniques are needed to compensate for the errors in the detection of lane-delimiting features. This paper presents a solution for lane estimation in difficult scenarios based on the particle-filtering framework. The solution employs a novel technique for pitch detection based on the fusion of two stereovision-based cues, a novel method for particle measurement and weighing using multiple lane-delimiting cues extracted by grayscale and stereo data processing, and a novel method for deciding upon the validity of the lane-estimation results. Initialization samples are used for uniform handling of the road discontinuities, eliminating the need for explicit track initialization. The resulting solution has proven to be a reliable and fast lane detector for difficult scenarios.