This paper presents a lane estimation technique based on the particle filter framework, which fuses several image-based cues (edges, lane markings and curbs), and 3D cues extracted from stereovision. A partition sampling-like approach is used to decouple pitch estimation from the rest of the parameter set, allowing the use of a significantly lower number of particles, and initialization samples are used for faster handling of discontinuous roads. We also introduce a measure for detection quality, for result validation. The resulted solution has proven to be a reliable and fast lane detector for difficult scenarios.
A stereovision-based probabilistic lane tracker for difficult road scenarios
2008 IEEE Intelligent Vehicles Symposium ; 536-541
2008-06-01
766676 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
A Stereovision-Based Probabilistic Lane Tracker for Difficult Road Scenarios
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