Virtual testing and validation are building blocks in the development of autonomous systems, in particular autonomous driving. Perception sensor models gained more attention to cover the entire tool chain of the sense–plan–act cycle, in a realistic test setup. In the literature or state-of-the-art software tools various kinds of lidar sensor models are available. We present a point cloud lidar sensor model, based on ray tracing, developed for a modular software architecture, which can be used stand-alone. The model is highly parametrizable and designed as a toolbox to simulate different kinds of lidar sensors. It is linked to an infrared material database to incorporate physical sensor effects introduced by the ray–surface interaction. The maximum detectable range depends on the material reflectivity, which can be covered with this approach. The angular dependence and maximum range for different Lambertian target materials are studied. Point clouds from a scene in an urban street environment are compared for different sensor parameters.

Within the EU Horizon 2020 project SHOW (GA No 875530) auxiliary measures, via evaluating and adapting physical road infrastructure (for instance lane markings, traffic signs, sight distances), as well as via digital support were explored for their potential contribution to enabling automated shared mobility services on the road environment. This line of research was then followed up on in the EU Horizon Europe Project AUGMENTED CCAM (GA 101,069,717), tackling more specifically with PDI support for automated mobility. This chapter presents the activities and findings of these two projects in relation to physical infrastructure adaptations for automated vehicles for two pilot sites in Austria.