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.
Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision
IEEE Transactions on Intelligent Transportation Systems ; 10 , 2 ; 272-282
2009-06-01
1013261 byte
Article (Journal)
Electronic Resource
English
Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision
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