A simulation-based framework for functional testing of automated driving controllers

Abstract

Motion planning is a major component of any automated driving system. The safety assessment of such components requires a formal characterization of the perception and control mechanisms. This requires dedicated tools and models for the environment, sensors and vehicles that are highly representative of the real world. Simulation is a method to virtually investigate the behavior of systems under study. It has a key role to play in demonstrating the safety of autonomous vehicles. In this context, we consider a control module as a black-box and try to determine a reference which represents the 'right decision', if it exists. An optimization-based reference model is created for the control function. This model allows each scene in the environment to be mapped to the desired decision regardless of the black-box. The black-box and the reference model are run on several critical scenarios. In output, an assessment of decision making is performed along with systematic criticality characterization of targeted scenarios.