# Real-World Scenario Mining for the Assessment of Automated Vehicles

arXiv: https://arxiv.org/abs/2006.00483 · HTML: https://mlsanity.com/paper/2006.00483/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 5 of 5)

- [Technical Report for Argoverse2 Scenario Mining Challenges on Iterative Error Correction and Spatially-Aware Prompting](https://mlsanity.com/paper/2506.11124/) (2025)
- [Why Braking? Scenario Extraction and Reasoning Utilizing LLM](https://mlsanity.com/paper/2507.15874/) (2025)
- [Comparing Normalizing Flows with Kernel Density Estimation in Estimating Risk of Automated Driving Systems](https://mlsanity.com/paper/2507.22429/) (2025)
- [SMc2f: Robust Scenario Mining for Robotic Autonomy from Coarse to Fine](https://mlsanity.com/paper/2601.12010/) (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)

Scenario-based methods for the assessment of Automated Vehicles (AVs) are widely supported by many players in the automotive field. Scenarios captured from real-world data can be used to define the scenarios for the assessment and to estimate their relevance. Therefore, different techniques are proposed for capturing scenarios from real-world data. In this paper, we propose a new method to capture scenarios from real-world data using a two-step approach. The first step consists in automatically labeling the data with tags. Second, we mine the scenarios, represented by a combination of tags, based on the labeled tags. One of the benefits of our approach is that the tags can be used to identify characteristics of a scenario that are shared among different type of scenarios. In this way, these characteristics need to be identified only once. Furthermore, the method is not specific for one type of scenario and, therefore, it can be applied to a large variety of scenarios. We provide two examples to illustrate the method. This paper is concluded with some promising future possibilities for our approach, such as automatic generation of scenarios for the assessment of automated vehicles.