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.
Correlation of Software-in-the-Loop Simulation with Physical Testing for Autonomous Driving
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abstract
Software-in-the-loop (SIL) simulation is a widely used method for the rapid development and testing of autonomous vehicles because of its flexibility and efficiency. This paper presents a case study on the validation of an in-house developed SIL simulation toolchain. The presented validation process involves the design and execution of a set of representative scenarios on the test track. To align the test track runs with the SIL simulations, a synchronization approach is proposed, which includes refining the scenarios by fine-tuning the parameters based on data obtained from vehicle testing. The paper also discusses two metrics used for evaluating the correlation between the SIL simulations and the vehicle testing logs. Preliminary results are presented to demonstrate the effectiveness of the proposed validation process
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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.