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The increase in complexity of autonomous systems is accompanied by a need ofdata-driven development and validation strategies. Advances in computergraphics and cloud clusters have opened the way to massive parallel highfidelity simulations to qualitatively address the large number of operationalscenarios. However, exploration of all possible scenarios is stillprohibitively expensive and outcomes of scenarios are generally unknownapriori. To this end, the authors propose a method based on bayesianoptimization to efficiently learn generative models on scenarios that woulddeliver desired outcomes (e.g. collisions) with high probability. Themethodology is integrated in an end-to-end framework, which uses theOpenSCENARIO standard to describe scenarios, and deploys highly configurabledigital twins of the scenario participants on a Virtual Test Bed cluster.

Finding Needles in Haystack: Formal Generative Models for Efficient Massive Parallel Simulations