# An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions

arXiv: https://arxiv.org/abs/2405.01776 · HTML: https://mlsanity.com/paper/2405.01776/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 8 of 8)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [ReDet: A Rotation-equivariant Detector for Aerial Object Detection](https://mlsanity.com/paper/2103.07733/) (2021)
- [iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images](https://mlsanity.com/paper/1905.12886/) (2019)
- [MMRotate: A Rotated Object Detection Benchmark using PyTorch](https://mlsanity.com/paper/2204.13317/) (2022)
- [Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art](https://mlsanity.com/paper/2108.02403/) (2021)

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

- [A Joint Approach Towards Data-Driven Virtual Testing for Automated Driving: The AVEAS Project](https://mlsanity.com/paper/2405.06286/) (2024)
- [Automatic Odometry-Less OpenDRIVE Generation From Sparse Point Clouds](https://mlsanity.com/paper/2405.07544/) (2024)
- [From Real-World Traffic Data to Relevant Critical Scenarios](https://mlsanity.com/paper/2512.07482/) (2025)

## Abstract (arXiv)

With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual environments and through 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, using their results accordingly. Especially in R&D areas related to the safety impact of the "open world", there is a significant shortage of real-world data to parameterize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated driving functions will meet in mixed traffic. We present an approach to systematically acquire data in public traffic by heterogeneous means, transform it into a unified representation, and use it to automatically parameterize traffic behavior models for use in data-driven virtual validation of automated driving functions.