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Modeling uncertainties pose a significant challenge in the development and deployment of model-based vehicle control systems. Most model- based automotive control systems require the use of a well estimated vehicle dynamics prediction model. The ability of first principles-based models to represent vehicle behavior becomes limited under complex scenarios due to underlying rigid physical assumptions. Additionally, the increasing complexity of these models to meet ever-increasing fidelity requirements presents challenges for obtaining analytical solutions as well as control design. Alternatively, deterministic data driven techniques including but not limited to deep neural networks, polynomial regression, Sparse Identification of Nonlinear Dynamics (SINDy) have been deployed for vehicle dynamics system identification and prediction.

Enabling self-driving vehicles to efficiently and autonomously navigate through an obstacle-filled environment remains a topic of significant contemporary research interest. Motion-planning frameworks, encapsulating both path- and trajectory-planning, have played a dominant role in realizing the deployment of a “sense-think-act” intelligence for autonomous vehicles. However, verification and validation of such intelligence on actual self-driving autonomous vehicles has been limited. Simulation-based verification and validation has the advantage of permitting diverse scenario-based testing and comprehensive “what-if” analyses - but is ultimately limited by the simulation fidelity and realism. In contrast, testing on full-scale real-world systems is constrained by the usual challenges of time, space, and cost engendered in reproducing diverse scenarios in practice.