Drivable space expansion from the ground base for complex structured roads

For driverless driving cars, it is essential to detect drivable space. It can directly apply to plan driving paths by acquiring the occupancy grid map. In addition, it can enhance object clustering by removing the ground in advance. However, in urban, not only a large number of vehicles are driving at the same time, but also roads with diverse inclinations are complicatedly connected with each other. Thus, it is challenging to extract traversable space properly from complex structured environment. For this reason, this paper proposes the real-time drivable space detection for complex urban environment by integrating the model-based segmentation and the region-based segmentation. Moreover, the proposed method utilizes point cloud from 3D LiDAR because it is effective to understand surrounding topography. It is demonstrated using hand-labeled point cloud dataset collected in various types of urban roads by estimating numerical performances and by visualizing results.