A Modular Architecture for Procedural Generation of Towns, Intersections and Scenarios for Testing Autonomous Vehicles

Simulation-based testing is critical for ensuring safety of autonomous vehicles. Autonomous vehicles are enabled by deep learning techniques which require a large quantity of data. With simulation testing, we can create rare events for testing and training of autonomous vehicles. Procedural generation of roads and modeling of driving behaviors in an easily extendable architecture ensures that we are able to create rare scenarios at scale with minimal artistic burden. In this paper, we present CruzWay, a system that both supports and creates these scenarios. With CruzWay, we are able to procedurally generate town sized road networks or road intersections. CruzWay supports generation of road meshes as well as navigation meshes from SUMO road network files. CruzWay can generate cars as well as pedestrians run by behavior trees (BTs) in this environment. The self-contained, modular nature of BTs in combination with procedural roads allows us to create a large number of scenarios.