Functional Gradient Descent Optimization for Automatic Test Case Generation for Vehicle Controllers

摘要

A hierarchical framework is proposed for improving the automatic test case generation process for high-fidelity models with long execution times. The framework incorporates related low-fidelity models for which certain properties can be analytically or computationally evaluated with provable guarantees (e.g., gradients of safety or performance metrics). The low-fidelity models drive the test case generation process for the high-fidelity models. The proposed framework is demonstrated on a model of a vehicle with Full Range Adaptive Cruise Control with Collision Avoidance (FRACC), for which it generates more challenging test cases on average compared to test cases generated using Simulated Annealing.