3D Geometry Modeling and Safety Compliance Assessment of In-Service Roads Using Massive LiDAR Point Clouds

It is essential to quantitatively evaluate road safety compliance through the modeling of three-dimensional geometric information of in-service roads to automatically guide the determination of driving speeds in intelligent transportation. A novel methodology is presented for reliably estimating 3D geometric elements and assessing geometry-related safety. A point-octree-normal-vector (PONV) network is developed to cope with the heterogeneous density of point clouds from mobile and terrestrial laser scanning and reduce possible failures caused by the disordered rotation of points. The points on road surfaces are adaptively segmented into stripes without data loss. Road horizontal elements, cross-sectional slopes and longitudinal slopes are estimated independent of alignment variations based on a concave hull algorithm, a cubic B-spline algorithm and a total least-squares method. A road safety compliance assessment related to the radius of curvature, average gradient, rate of curvature changes and side friction factors is conducted. Warning and suggested driving speeds are thus provided. Experiments show that the PONV network achieved superior performance in extracting road surfaces, with accuracy, precision, recall, and mean intersection-over-union scores of 98.1%, 89.7%, 89.3% and 82.5%, respectively. The geometric elements with neighboring distances in meters were basically consistent with those in traditional surveys and raw point clouds. Through comparison with safety guidelines, suitable vehicle operation speeds were suggested in road sections with unreasonable rates of curvature changes and side friction factors. This work can be applied for constructing road information models and automatically guiding driving speeds with high-definition maps on autonomous vehicles.