TOWARDS DETECTING SPEED BUMPS FROM MLS POINT CLOUDS DATA

Abstract. High definition maps contain a high level of information about the road and its features. This includes traffic signs, speed limits and lane markings, that are not generally included on regular maps. However, they may still lack critical information, such as the locations of speed bumps. Without the information about the whereabouts of speed bumps the passengers’ comfort and safety, as well as the condition of the car may be jeopardized. There are currently methods for detecting speed bumps that use changes in acceleration or 3D cameras. However, these approaches are susceptible to external influences interfering with the recorded data. Hence, it is desirable to have this information stored and available through HD maps. In this work, we tested two deep learning-based approaches (PointNet++ and PointCNN) and compared the results with conventional region growing method, in order to find out pros and cons of the modern deep learning-based methods. For our test, we used MLS (mobile laser scanning) point clouds data in Trondheim, Norway.