Multi-lane detection based on accurate geometric lane estimation in highway scenarios

Abstract

Multi-lane detection algorithms have been required for various vehicle safety-related applications. In most of the previous study, visual features are fundamental clues for multi-lane detection. However, since visual features vary with the illumination, weather condition and distance of the region, feature-based algorithm is restricted to the illuminant variation and laterally adjacent regions. On the other hand, conventional geometric estimation-based approaches, not relying on visual features, are inaccurate and susceptible to pitch or lateral movement. In this paper, we propose a robust multi-lane detection algorithm based on the accurate geometric estimation in highway scenarios. With the steps of adjacent lanes hypothesis generation (HG) and hypothesis verification (HV), the algorithm detects successfully independent of environmental changes. For accurate adjacent lane HG, we adopt the `cross ratio' and propose `dynamic homography matrix estimation.' Our approach is independent of the calibration, pitch angle changes and additional vehicle sensors. In addition, the proposed algorithm can covers six lanes including the driving lane and adjacent lanes that two-lanes away from the driving lane. We demonstrate robustness on the illumination variance including daytime, rainy and sunset by using trough simulations and video sequences with a resolution of 752 × 480.