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

TL;DR

This paper presents a clustering pipeline using CVQ-VAE to analyze and evaluate the completeness of traffic scenario categories for automated highway driving, aiding in safer system deployment.

Contribution

It introduces a novel clustering approach for traffic scenarios and evaluates how category granularity affects scenario completeness for ADS safety.

Findings

CVQ-VAE outperforms previous clustering methods

Trade-off identified between cluster quality and data requirements

Analysis based on highD dataset demonstrates practical applicability

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.