Drivable area detection is one of the essential functions of autonomous vehicles. However, due to the complexity and diversity of unknown environments, it remains a challenge specifically on rough roads. In this paper, we propose a systematical framework for drivable area detection, including ground segmentation and road labelling. For each scan, the point cloud is projected onto two different planes simultaneously, generating an elevation map and a range map. Different from the existing methods based on mathematical models, we accomplish the ground segmentation using image processing methods. Subsequently, road points will be filtered out from ground points and used to generate the road area with the assistance of a range map. Meanwhile, a two-step search method is utilized to create the reachable area from an elevation map. For the robustness of drivable area detection, Bayesian decision theory is introduced in the final step to fuse the road area and the reachable area. Since we explicitly avoid complex three-dimensional computation, our method, from both empirical and theoretical perspectives, has a high real-time capability, and experimental results also show it has promising detection performance in various traffic situations.

可驾驶区域检测是自动驾驶汽车的基本功能之一。但是，由于未知环境的复杂性和多样性，特别是在崎rough不平的道路上，这仍然是一个挑战。在本文中，我们提出了一个可驾驶区域检测的系统框架，包括地面分割和道路标记。对于每次扫描，将点云同时投影到两个不同的平面上，生成高程图和距离图。与基于数学模型的现有方法不同，我们使用图像处理方法完成地面分割。随后，道路点将从地面点中滤出，并在范围图的帮助下用于生成道路区域。同时，利用两步搜索方法从高程图创建可到达区域。为了确保可行驶区域检测的鲁棒性，在最后一步中引入了贝叶斯决策理论，以融合道路区域和可到达区域。由于我们明确避免了复杂的三维计算，因此从经验和理论角度来看，我们的方法都具有较高的实时能力，并且实验结果还表明，该方法在各种交通情况下均具有良好的检测性能。