A Bayesian optimization framework finds critical scenarios for an MPC motion planner using one to two orders of magnitude fewer simulations than full-factorial testing.
Finding critical scenarios for automated driving systems: A systematic mapping study,
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Bayesian Optimization applied for accelerated Virtual Validation of the Autonomous Driving Function
A Bayesian optimization framework finds critical scenarios for an MPC motion planner using one to two orders of magnitude fewer simulations than full-factorial testing.