Scenario-based testing is envisioned as a key approach for the safety assurance of autonomous vehicles. In scenario-based testing, relevant (driving) scenarios are the basis of tests. Many recent works focus on specification, variation, generation and execution of individual scenarios. In this work, we address the open challenges of classifying sets of scenarios and measuring coverage of theses scenarios in recorded test drives. Technically, we define logic-based classifiers that compute features of scenarios on complex data streams and combine these classifiers into feature trees that describe sets of scenarios. We demonstrate the expressiveness and effectiveness of our approach by defining a scenario classifier for urban driving and evaluating it on data recorded from simulations.
@article{arxiv.2307.05106,
title = {Tree-Based Scenario Classification: A Formal Framework for Coverage Analysis on Test Drives of Autonomous Vehicles},
author = {Till Schallau and Stefan Naujokat and Fiona Kullmann and Falk Howar},
journal= {arXiv preprint arXiv:2307.05106},
year = {2023}
}