Building a Library of Scenarios for Testing ML Models

Creating a library of scenarios is crucial for testing self-driving cars and other machine learning models. These scenarios include sensor data, hard detection tasks, simulation scenarios, and real-world data. By collecting and categorizing scenarios generated by requirements or iteration, models can be improved and tested effectively to ensure optimal performance across various use cases.