@article{ref1,
title="Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision",
journal="IEEE transactions on intelligent transportation systems",
year="2009",
author="Danescu, R. and Nedevschi, S.",
volume="10",
number="2",
pages="272-282",
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.<p />",
language="",
issn="1524-9050",
doi="10.1109/TITS.2009.2018328",
url="http://dx.doi.org/10.1109/TITS.2009.2018328"
}