Optimizing Virtual Scenario Testing for Autonomous Vehicles with Large-Scale Scenario Generation.

The validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) Systems, especially at higher automation levels such as SAE Level 3 or 4, demands the testing of a vast array of scenario variants far exceeding the scope of standard safety specifications like Euro NCAP (The European New Car Assessment Programme). Autonomous vehicles require thorough real-world testing to ensure automotive safety. However, public road tests are costly and risky. Instead, virtual scenarios - digital twins of real environments - offer a safe, cost-effective testing alternative. Exhaustive simulation across this high-dimensional scenario space, which includes variations in actor behavior, environmental conditions, and event characteristics, is computationally infeasible. We propose a constraint-solving approach to address this challenge that leverages mathematical and geometric techniques to analytically assess the existence and validity of scenario variants prior to simulation. Two primary methods are explored: (1) random or sequential generation of scenario variants with a pre-simulation pruning step to eliminate invalid cases, and (2) direct generation of valid variants by solving constraint systems that ensure the desired events occur under specified conditions. Importantly, maintaining an effective balance between these two approaches is central to our methodology, as the optimal mix depends on the specific testing goals and requirements. This framework, implemented using MATLAB®, Simulink®, the Automated Driving ToolboxTM, and the Euro NCAP Support Package®, systematically reduces the scenario space by excluding impossible cases. Our approach aims to significantly reduce reliance on extensive simulation and enable more targeted and efficient validation for safety compliance.