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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.

Recent trends in autonomy have emphasized end-to-end deep-learning-based methods that have shown a lot of promise in overcoming the requirements and limitations of feature-engineering. However, while promising, the black-box nature of deep-learning frameworks now exacerbates the need for testing with end-to-end deployments. Further, as exemplars of systems-of-systems, autonomous vehicles (AVs) engender numerous interconnected component-, subsystem and system-level interactions. The ensuing complexity creates challenges for verification and validation at the various component, subsystem- and system-levels as well as end-to-end testing. While simulation-based testing is one promising avenue, oftentimes the lack of adequate fidelity of AV and environmental modeling limits the generalizability. In contrast, full-scale AV testing presents the usual limitations of time-, space-, and cost.

Artificial intelligence (AI) enhanced control system deployments are emerging as a viable substitute to more traditional control system. In particular, deep learning techniques offer an alternate approach to tune the ever increasing sets of control system parameters to extract performance. However, the systematic verification and validation (to establish the reliability and robustness) of deep learning based controllers in actual deployments remains a challenge. This is exacerbated by the need to evaluate and optimize control systems embedded within an operational environment (with its own sets of additional unknown or uncertain parameters). Existing literature comparisons of deep learning against traditional controllers, where they may exist, do not offer structured approaches to comparative performance evaluation and improvement. It is also crucial to develop a standardized controlled test environment within which various controllers are evaluated against a common metric.