Visibility Estimation in Complex, Real-World Driving Environments Using High Definition Maps

As the autonomy level of self-driving vehicles increases, they will be expected to operate safely in increasingly complex environments. During real-world driving, occlusions are inevitable. Therefore, the ability to accurately identify the visible and occluded regions surrounding an autonomous vehicle is crucial for safe operation. In this paper, a method for estimating visibility using 3D point clouds and road network maps is proposed. The proposed method projects the positions of the surrounding lanes, obtained from a road network map, and a 3D scan of the driving environment approximated from a point cloud map, onto depth images. The depth images are then compared in order to determine the visible and occluded regions of the driving environment from the specified viewpoint. Furthermore, a visibility ratio, which is a numerical value that encapsulates visibility information of a particular location, is proposed. The visibility ratio is calculated by dividing the visible area of interest by the total driving area relevant to that location. The proposed method was tested and found to be applicable in both simulated and real-world driving environments. Moreover, the experimental results show that the visibility ratio was representative of the actual visibility from particular locations.