mlsanityData to 14 Oct 2026

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

arXiv 2506.02599 ↗First posted 3 Jun 2025cs.LGcited by 3

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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.

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

20 of 31 references.

References · 20

  1. 2017Neural Discrete Representation Learningcited by 4,712
  2. 2023GPT-Driver: Learning to Drive with GPTcited by 347
  3. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 277
  4. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 114
  5. 2023Online Clustered Codebookcited by 55
  6. 2021Max-Margin Contrastive Learningcited by 27
  7. 2019VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019cited by 25
  8. 2021Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Spacecited by 21
  9. 2024Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Modelscited by 13
  10. 2020Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classificationcited by 8
  11. 2023A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Predictioncited by 8
  12. 2020Deep Representation Learning and Clustering of Traffic Scenarioscited by 7
8 more
  1. 2023Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representationcited by 5
  2. 2024Coverage Metrics for a Scenario Database for the Scenario-Based Assessment of Automated Driving Systemscited by 4
  3. 2023Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applicationscited by 3
  4. 2024Learning Sampling Distribution and Safety Filter for Autonomous Driving with VQ-VAE and Differentiable Optimizationcited by 3
  5. 2021The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenXcited by 2
  6. 2023Tree-Based Scenario Classification: A Formal Framework for Coverage Analysis on Test Drives of Autonomous Vehiclescited by 2
  7. 2023Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Losscited by 1
  8. 2024Towards a Completeness Argumentation for Scenario Conceptscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddingscited by 0
  2. 2026Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Samplingcited by 0
  3. 2026Behavior-Centric Extraction of Scenarios from Highway Traffic Data and their Domain-Knowledge-Guided Clustering using CVQ-VAEcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Zero-Label Driving Scenario Complexity Detection via Joint Embedding Predictive Architecture
  2. 2023ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling
  3. 2024LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization
  4. 2026REARL: A Closed-loop Autonomous Driving Simulation Enhancement Framework with Real Traffic Data and Large Language Models
  5. 2025Learning from Risk: LLM-Guided Generation of Safety-Critical Scenarios with Prior Knowledge
  6. 2025Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications
  7. 2025SAFE: Harnessing LLM for Scenario-Driven ADS Testing from Multimodal Crash Data ↗
  8. 2025DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models