An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios
This work addresses the challenge of scenario selection for testing automated driving systems, which is incremental as it applies existing optimization methods to a specific domain.
The paper tackled the problem of identifying relevant scenarios for automated driving system testing by using Bayesian optimization and Gaussian processes to find critical parameter sets in urban intersection scenarios, evaluating six different metrics.
Verification and validation are major challenges for developing automated driving systems. A concept that gets more and more recognized for testing in automated driving is scenario-based testing. However, it introduces the problem of what scenarios are relevant for testing and which are not. This work aims to find relevant, interesting, or critical parameter sets within logical scenarios by utilizing Bayes optimization and Gaussian processes. The parameter optimization is done by comparing and evaluating six different metrics in two urban intersection scenarios. Finally, a list of ideas this work leads to and should be investigated further is presented.