Monocular Camera-based Road Segmentation Using Geometric Cues

Abstract

Vision-based road segmentation aims to identify drivable road regions in images captured by vehicle-mounted cameras. With the rapid development of autonomous driving, such methods have attracted substantial attention from both academia and industry due to their research significance and commercial potential. However, a key challenge lies in achieving robust adaptation—that is, effectively generalizing to diverse environmental and operational conditions.

In my Ph.D. research, I have concentrated on leveraging geometric information to advance monocular-camera-based road segmentation and adaptation. Specifically, I have completed three independent projects: (1) a supervised road segmentation approach that fuses geometric cues with appearance cues to enhance segmentation accuracy, (2) an unsupervised domain adaptation method that employs a novel geometry-guided strategy to reduce domain shift between source and target data, and (3) another unsupervised domain adaptation approach that uses known camera parameters and online estimation of the camera pan and tilt to better align imagery across and within datasets, improving accuracy and generalization. These contributions collectively push forward the development of robust, geometry-driven solutions for road scene understanding.