A superparticle filter for lane detection

We extend previously defined particle filters for lane detection by using a more general lane model supporting the use of two independent particle filters for detecting left and right lane borders separately, by combining multiple particles, traditionally used for identifying a winning particle in one image row, into one superparticle, and by using local linear regression for adjusting detected border points. The combination of multiple particles makes it possible to extend the traditional emphasis of particle-filter-based lane detectors (on identifying sequences of isolated border points) towards a local approximation of lane borders by polygonal or smooth curves further detailed in our local linear regression. The paper shows by experimental studies that results, obtained by the proposed novel lane detection procedure, improve compared to previously achieved particle-filter-based results especially for challenging lane detection situations. The presentation of several methods for comparative performance evaluation is another contribution of this paper.