# A Joint Approach Towards Data-Driven Virtual Testing for Automated Driving: The AVEAS Project

arXiv: https://arxiv.org/abs/2405.06286 · HTML: https://mlsanity.com/paper/2405.06286/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 1 of 1)

- [An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions](https://mlsanity.com/paper/2405.01776/) (2024)

## Built on by (most cited first, 2 of 2)

- [Physically-Based Simulation of Automotive LiDAR](https://mlsanity.com/paper/2512.05932/) (2025)
- [From Real-World Traffic Data to Relevant Critical Scenarios](https://mlsanity.com/paper/2512.07482/) (2025)

## Abstract (arXiv)

With growing complexity and responsibility of automated driving functions in road traffic and growing scope of their operational design domains, there is increasing demand for covering significant parts of development, validation, and verification via virtual environments and simulation models. If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus allow their usage for virtual testing of driving functions. Especially in research and development areas related to the safety impacts of the "open world", there is a significant shortage of real-world data to parametrize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated vehicles will meet in mixed traffic. This paper presents the intermediate results of the German AVEAS research project (www.aveas.org) which aims at developing methods and metrics for the harmonized, systematic, and scalable acquisition of real-world data for virtual verification and validation of advanced driver assistance systems and automated driving, and establishing an online database following the FAIR principles.