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Article overview
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Finding Needles in Haystack: Formal Generative Models for Efficient Massive Parallel Simulations | | Osama Maqbool
; Jürgen Roßmann
; | | Date: |
3 Jan 2023 | | Abstract: | The increase in complexity of autonomous systems is accompanied by a need of
data-driven development and validation strategies. Advances in computer
graphics and cloud clusters have opened the way to massive parallel high
fidelity simulations to qualitatively address the large number of operational
scenarios. However, exploration of all possible scenarios is still
prohibitively expensive and outcomes of scenarios are generally unknown
apriori. To this end, the authors propose a method based on bayesian
optimization to efficiently learn generative models on scenarios that would
deliver desired outcomes (e.g. collisions) with high probability. The
methodology is integrated in an end-to-end framework, which uses the
OpenSCENARIO standard to describe scenarios, and deploys highly configurable
digital twins of the scenario participants on a Virtual Test Bed cluster. | | Source: | arXiv, 2301.01594 | | Services: | Forum | Review | PDF | Favorites | |
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