Automatic 3D Road Surface Reconstruction via Cross-Section Modeling and Interpolation

Accurate 3D road surfaces are important for the development of detailed and realistic scenarios to validate autonomous driving algorithms. In these scenarios, simulations can be conducted, for instance, to evaluate the response of a safety system under dangerous conditions. In this paper, we propose an approach designed to automatically generate 3D road surfaces from data collected by a vehicle equipped with various sensors, including a LiDAR. These road surfaces are meant to be both accurate and realistic for driving simulations. The proposed approach, after deriving the clothoidal represen- tation of the surface borders, pursues the idea of extracting and interpolating a set of smooth 3D cross-section profiles. The resulting surface provides a 3D representation in analytical form, allowing detailed rendering at the desired resolution. We experimentally evaluate the proposed approach in a real- world scenario to assess its performance in terms of accuracy, scalability, and computing time.