Scopul nostru este sprijinirea şi promovarea cercetării ştiinţifice şi facilitarea comunicării între cercetătorii români din întreaga lume.
Autori: Radu Danescu, Sergiu Nedevschi
Editorial: Alberto Broggi, IEEE, IEEE Transactions on Intelligent Transportation Systems, 10(2), p.272-282, 2009.
Accurate and robust lane results are of great significance in any driving assistance system. In order to achieve robustness and accuracy in difficult scenarios, probabilistic estimation techniques are needed to compensate for the errors in detection of lane delimiting features. The 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 fusion of two stereovision-based cues, a novel method for particle measurement and weighting 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 resulted solution has proven to be a reliable and fast lane detector for difficult scenarios.
Cuvinte cheie: lane detection, tracking, particle filtering, cue fusion, stereovision