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

TL;DR

This paper tackles safety certification for automated highway driving by representing observed traffic as a finite catalog of scenarios with $Q$ categories and by using a Coupon Collectors Problem (CCP) based completeness test with confidence $\\tau$ to determine how much data is needed. It introduces a CVQ-VAE-based clustering pipeline that employs a dynamically updated codebook to generate scenario catalogs and a CCP-based completeness check to quantify data requirements for complete coverage. Evaluated on the highD dataset, the approach achieves improved codebook utilization and lower reconstruction loss as codebook size grows, though gains in classification accuracy do not always scale with more categories. The results reveal a trade-off between the number of scenario categories and data requirements, and point to the need for a robust metric to select the optimal catalog size for ADS validation.

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.

Figures (6)

• Figure 1: Pipeline for identifying scenario categories and evaluating data completeness based on Neumeier2024Hauer2019.
• Figure 2: The proposed pipeline in detail consists of: the CVQ-VAE, a future behavior predictor and the completeness check. The CVQ-VAE discretizes the driving scenario $\bm{\xi}$. Its performance is improved by the future behavior predictor. The codebook $Q$ created in the process contains the various driving scenario categories $q$ and their probabilities of occurrence $p_q$. The completeness check uses these category probabilities $p_q$ and the hyperparameters $p_{\text{new}}$ and $\tau$ to compute the required amount of data $S_{\text{min}}$ needed to achieve scenario category completeness with confidence $\tau$.
• Figure 3: Stacked histogram showing the codebook usage in (a) the work Neumeier2024 compared to (b) this study for the codebook size $Q_1 =64$. Colors indicate the ground truth future behavior class. The histogram has a logarithmic scale.
• Figure 4: Confusion matrices for model $Q_1$, $Q_2$ and $Q_3$ showing the class prediction accuracy.
• Figure 5: (a) Visualized vehicle trajectories of multiple driving scenarios assigned to the codebook entry $q = 167$ of $Q=253$ and (b) the corresponding generated vehicle trajectories $\bm{\hat{\xi}}$ for the representative scenario of $q=167$.