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Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision

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2 Author(s)
Danescu, R. ; Tech. Univ. of Cluj-Napoca, Cluj-Napoca ; Nedevschi, S.

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.

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:10 ,  Issue: 2 )