Automated Driving in Complex Real-World Scenarios using a Scalable Risk-Based Behavior Generation Framework

The task of driving autonomously is difficult due to the vast number of driving situations a system may be facing. Especially higher levels of automation in less restricted scopes remain a topic of active research. In previous work, we introduced a behavior planning system which uses analytic models to evaluate the quality of behavior holistically. It uses these models to generate quality-maximizing behavior instead of selecting among predefined behavior primitives. The system was able to solve various complex urban traffic scenarios in large-scale simulations. In this paper, we verify the system using multiple prototype vehicles on proving grounds in a number of difficult urban scenarios such as prioritized intersections or overtaking. We describe the system architecture and principles which render the system embodiment-agnostic and make extensions for additional features possible without massively increasing the complexity.