Nonparametric Technique Based High-Speed Road Surface Detection

It has been well recognized that detecting road surface in a realistic environment is a challenging problem that is also computationally intensive. Existing road surface detection methods attempt to fit the road surface into rigid models (e.g., planar, clothoid, or B-Spline), thereby restricting to road surfaces that match specific models. In addition, the curve-fitting strategies employed in such techniques incur high computational complexity, making them unsuitable for in-vehicle deployments. In this paper, we propose an efficient nonparametric road surface detection algorithm that exploits the depth cue. The proposed method relies on four intrinsic road scene attributes observed under stereo geometry and has been shown to reliably detect both planar and nonplanar road surfaces efficiently. Extensive evaluations are performed on three widely used benchmarks (i.e., enpeda, KITTI, and Daimler), encompassing many complex road scenarios. The experimental results show that the proposed algorithm significantly outperforms the well-known techniques both in terms of detection accuracy and runtime performance.