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Environment perception is an essential component in autonomous driving technology. Curb is one of the most prominent features on an urban road, which defines the boundary of a road surface. An intersection is a junction of two or more roads, appearing where no curb exists. Intersection recognition and curb detection are significant in environment perception which ensure the performance of Unmanned Ground Vehicles (UGVs). In this paper, a novel double layer beam model is proposed to recognise the intersection shape and classify the road type in front of the UGV, and then based on the current road type and spatial features, a real-time road boundary detection algorithm is proposed to extract the curb position. The performance of the proposed method is verified through extensive experiments with a UGV autonomously driving on campus roads. The experimental results demonstrate the accurate and robust performance of the proposed algorithm.
Zhang et al. (Tue,) studied this question.