Behavior-Centric Extraction of Scenarios from Highway Traffic Data and their Domain-Knowledge-Guided Clustering using CVQ-VAE

2026-03-17

AI Summary

This work addresses the lack of standardized traffic scenario extraction and the absence of domain knowledge in clustering for autonomous driving validation. To this end, it proposes a behavior-centric scenario extraction method grounded in the “Scenario-as-Specification” paradigm and introduces a class-vector quantized variational autoencoder (CVQ-VAE) that integrates prior driving rules and other domain-specific knowledge to enable interpretable clustering. Evaluated on the highD dataset, the approach achieves standardized, high-fidelity scenario extraction and yields semantically coherent clusters, substantially enhancing the systematic coverage of driving scenarios and the interpretability of the validation pipeline. This provides an efficient and well-structured foundation for autonomous driving testing and verification.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Machine Learning: Clustering
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Approval of ADS depends on evaluating its behavior within representative real-world traffic scenarios. A common way to obtain such scenarios is to extract them from real-world data recordings. These can then be grouped and serve as basis on which the ADS is subsequently tested. This poses two central challenges: how scenarios are extracted and how they are grouped. Existing extraction methods rely on heterogeneous definitions, hindering scenario comparability. For the grouping of scenarios, rule-based or ML-based methods can be utilized. However, while modern ML-based approaches can handle the complexity of traffic scenarios, unlike rule-based approaches, they lack interpretability and may not align with domain-knowledge. This work contributes to a standardized scenario extraction based on the Scenario-as-Specification concept, as well as a domain-knowledge-guided scenario clustering process. Experiments on the highD dataset demonstrate that scenarios can be extracted reliably and that domain-knowledge can be effectively integrated into the clustering process. As a result, the proposed methodology supports a more standardized process for deriving scenario categories from highway data recordings and thus enables a more efficient validation process of automated vehicles.

Problem

• scenario extraction
• scenario clustering
• domain knowledge
• automated driving systems
• traffic data

Innovation

• Scenario-as-Specification
• domain-knowledge-guided clustering
• CVQ-VAE
• scenario extraction
• automated driving validation