用聚类分析评估高速自动驾驶场景分类的完整性，发现类别数量与数据需求间的权衡。
Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-based Analysis
- 基于CVQ-VAE模型对高速交通场景进行聚类。
- 在highD数据集上验证了聚类性能优于以往方法。
- 揭示了场景类别数与数据量之间的完整度权衡关系。
自动驾驶系统在日益复杂的交通场景中安全运行是基本要求，确保其安全发布需精确理解实际发生的交通场景。本文提出一种交通场景聚类与分类完整性分析的流程。采用基于向量量化变分自编码器（CVQ-VAE）对高速公路交通场景进行聚类，并构建不同类别数量的场景目录。随后分析类别数量对场景分类完整性的影响。结果表明，该方法在聚类性能上优于先前工作。基于公开的highD数据集，探讨了聚类质量与维持完整性所需数据量之间的权衡关系。
原文摘要 · Abstract (English)
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.
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