# Behavior-Centric Extraction of Scenarios from Highway Traffic Data and their Domain-Knowledge-Guided Clustering using CVQ-VAE

arXiv: https://arxiv.org/abs/2603.16964 · HTML: https://mlsanity.com/paper/2603.16964/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 18 of 18)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [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)
- [Online Clustered Codebook](https://mlsanity.com/paper/2307.15139/) (2023)
- [Fundamental Considerations around Scenario-Based Testing for Automated Driving](https://mlsanity.com/paper/2005.04045/) (2020)
- [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)
- [Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification](https://mlsanity.com/paper/2211.06463/) (2022)
- [Real-World Scenario Mining for the Assessment of Automated Vehicles](https://mlsanity.com/paper/2006.00483/) (2020)
- [Traffic Scene Similarity: a Graph-based Contrastive Learning Approach](https://mlsanity.com/paper/2309.09720/) (2023)
- [VistaScenario: Interaction Scenario Engineering for Vehicles with Intelligent Systems for Transport Automation](https://mlsanity.com/paper/2402.07720/) (2024)
- [Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction](https://mlsanity.com/paper/2103.16101/) (2021)
- [Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity](https://mlsanity.com/paper/2105.07639/) (2021)
- [Scenario Extraction from a Large Real-World Dataset for the Assessment of Automated Vehicles](https://mlsanity.com/paper/2306.07815/) (2023)
- [Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-based Analysis](https://mlsanity.com/paper/2506.02599/) (2025)
- [Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios](https://mlsanity.com/paper/2207.09120/) (2022)
- [Unsupervised Driving Event Discovery Based on Vehicle CAN-data](https://mlsanity.com/paper/2301.04988/) (2023)
- [scenario.center: Methods from Real-world Data to a Scenario Database](https://mlsanity.com/paper/2404.02561/) (2024)
- [TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility](https://mlsanity.com/paper/2505.06743/) (2025)

## Abstract (arXiv)

Approval of ADS depends on evaluating its behavior within representative real-world traffic scenarios. A common way to obtain such scenarios is to extract them from real-world data recordings. These can then be grouped and serve as basis on which the ADS is subsequently tested. This poses two central challenges: how scenarios are extracted and how they are grouped. Existing extraction methods rely on heterogeneous definitions, hindering scenario comparability. For the grouping of scenarios, rule-based or ML-based methods can be utilized. However, while modern ML-based approaches can handle the complexity of traffic scenarios, unlike rule-based approaches, they lack interpretability and may not align with domain-knowledge. This work contributes to a standardized scenario extraction based on the Scenario-as-Specification concept, as well as a domain-knowledge-guided scenario clustering process. Experiments on the highD dataset demonstrate that scenarios can be extracted reliably and that domain-knowledge can be effectively integrated into the clustering process. As a result, the proposed methodology supports a more standardized process for deriving scenario categories from highway data recordings and thus enables a more efficient validation process of automated vehicles.