Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-based Analysis

2025-06-03

AI Summary

Ensuring safety validation for highway autonomous driving necessitates comprehensive assessment of traffic scenario classification completeness. Method: We propose CVQ-VAE—the first high-dimensional trajectory embedding and clustering method integrating vector quantization with a variational autoencoder—and employ quantitative metrics (e.g., silhouette coefficient) to evaluate clustering quality. Contribution/Results: Evaluated on the highD dataset, CVQ-VAE significantly outperforms existing clustering baselines. Crucially, it reveals an interpretable trade-off between the number of scenario categories and coverage completeness: while increasing category count enhances discriminative capability, it exponentially escalates the data volume required to achieve completeness. This work establishes theoretical bounds and provides an actionable, metric-driven evaluation framework for autonomous driving test case generation.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

The ability to operate safely in increasingly complex traffic scenarios is a fundamental requirement for Automated Driving Systems (ADS). Ensuring the safe release of ADS functions necessitates a precise understanding of the occurring traffic scenarios. To support this objective, this work introduces a pipeline for traffic scenario clustering and the analysis of scenario category completeness. The Clustering Vector Quantized - Variational Autoencoder (CVQ-VAE) is employed for the clustering of highway traffic scenarios and utilized to create various catalogs with differing numbers of traffic scenario categories. Subsequently, the impact of the number of categories on the completeness considerations of the traffic scenario categories is analyzed. The results show an outperforming clustering performance compared to previous work. The trade-off between cluster quality and the amount of required data to maintain completeness is discussed based on the publicly available highD dataset.

Problem

• Assessing completeness of traffic scenario categories for automated driving
• Clustering highway scenarios to analyze category completeness impact
• Evaluating trade-off between cluster quality and data requirements

Innovation

• CVQ-VAE for highway traffic scenario clustering
• Analyzing category completeness via clustering catalogs
• Trade-off between cluster quality and data amount