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

arXiv: https://arxiv.org/abs/2506.02599 · HTML: https://mlsanity.com/paper/2506.02599/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Online Clustered Codebook](https://mlsanity.com/paper/2307.15139/) (2023)
- [Max-Margin Contrastive Learning](https://mlsanity.com/paper/2112.11450/) (2021)
- [VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019](https://mlsanity.com/paper/1905.11449/) (2019)
- [Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space](https://mlsanity.com/paper/2103.13726/) (2021)
- [Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models](https://mlsanity.com/paper/2405.14384/) (2024)
- [Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification](https://mlsanity.com/paper/2004.02126/) (2020)
- [A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2305.07416/) (2023)
- [Deep Representation Learning and Clustering of Traffic Scenarios](https://mlsanity.com/paper/2007.07740/) (2020)
- [Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation](https://mlsanity.com/paper/2309.09733/) (2023)
- [Coverage Metrics for a Scenario Database for the Scenario-Based Assessment of Automated Driving Systems](https://mlsanity.com/paper/2409.01139/) (2024)
- [Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications](https://mlsanity.com/paper/2305.16196/) (2023)
- [Learning Sampling Distribution and Safety Filter for Autonomous Driving with VQ-VAE and Differentiable Optimization](https://mlsanity.com/paper/2403.19461/) (2024)
- [The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX](https://mlsanity.com/paper/2103.09772/) (2021)
- [Tree-Based Scenario Classification: A Formal Framework for Coverage Analysis on Test Drives of Autonomous Vehicles](https://mlsanity.com/paper/2307.05106/) (2023)
- [Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss](https://mlsanity.com/paper/2311.12986/) (2023)
- [Towards a Completeness Argumentation for Scenario Concepts](https://mlsanity.com/paper/2404.01934/) (2024)

## Built on by (most cited first, 3 of 3)

- [Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddings](https://mlsanity.com/paper/2602.09985/) (2026)
- [Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Sampling](https://mlsanity.com/paper/2602.21319/) (2026)
- [Behavior-Centric Extraction of Scenarios from Highway Traffic Data and their Domain-Knowledge-Guided Clustering using CVQ-VAE](https://mlsanity.com/paper/2603.16964/) (2026)

## Abstract (arXiv)

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.