Adaptive and robust road tracking system based on stereovision and particle filtering

Abstract

In order to achieve robust and accurate lane detection results in difficult scenarios, probabilistic estimation techniques are needed to compensate for the errors in detecting the 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 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. The working range of the lane detection algorithm is automatically determined based on vehicle speed and the availability of 3D data points. Initialization samples are used for uniform handling of the road discontinuities, eliminating the need for explicit track initialization.