Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification
This addresses the safety testing bottleneck for automated vehicles, offering a more efficient approach to identify critical scenarios, though it appears incremental as it builds on existing classification methods.
The paper tackles the problem of identifying challenging corner-case scenarios for automated vehicle testing by proposing a method using Gaussian Process Classification to locate the performance boundary, demonstrating feasibility in an exemplary traffic jam scenario for more efficient testing.
Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenarios. Handling of the most challenging scenarios should be tested, which leads to the question of how such corner cases can be determined. We propose an approach to identify the performance boundary, where these corner cases are located, using Gaussian Process Classification. We also demonstrate the classification on an exemplary traffic jam approach scenario, showing that it is feasible and would lead to more efficient testing practices.