Over the years, the complexity of autonomous vehicle development (and concurrently the verification and validation) has grown tremendously in terms of component-, subsystem- and system-level interactions between autonomy and the human users. Simulation-based testing holds significant promise in helping to identify both problematic interactions between component-, subsystem-, and system-levels as well as overcoming delays typically introduced by the default full-scale on-road testing. Software in Loop (SiL) simulation is utilized as an intermediate step towards software deployment for autonomous vehicles (AV) to make them reliable. SiL efforts can help reduce the resources required for successful deployment by helping to validate the software for millions of road miles. A key enabler for accelerating SiL processes is the ability to use Simulation as a Service (SaaS) rather than just isolated instances of software. The primary benefits ensue from the in-parallel processing of multiple scenarios or tests using cloud or multiple cores especially to more systematically create “what-if analyses” thereby reducing both development time and cost. Here, we present the workflow of our utilization of SaaS methods (provisioned by Metamoto) and our explorations in this domain using exemplar ADAS scenarios. Additionally, we highlight our ability to perform parametric sweeps over variables such as environmental conditions, actors in the scene, etc. hence performing tests over a variety of scenarios including edge cases. The goal of our efforts is to examine viability and ease-of-use of SaaS (Metamoto in a co-simulation mode) to support Software-in-the-Loop co-development and functional reliability within MATLAB, ROS and Python frameworks.
