# Fast and Realistic Automated Scenario Simulations and Reporting for an Autonomous Racing Stack

arXiv: https://arxiv.org/abs/2512.24402 · HTML: https://mlsanity.com/paper/2512.24402/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 30 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge](https://mlsanity.com/paper/2205.15979/) (2022)
- [Scalable Supervisory Architecture for Autonomous Race Cars](https://mlsanity.com/paper/2408.15049/) (2024)
- [HALO: Fault-Tolerant Safety Architecture For High-Speed Autonomous Racing](https://mlsanity.com/paper/2503.10341/) (2025)
- [Approaching Current Challenges in Developing a Software Stack for Fully Autonomous Driving](https://mlsanity.com/paper/2504.12813/) (2025)

## Built on by (most cited first, 1 of 1)

- [Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyond](https://mlsanity.com/paper/2609.12660/) (2026)

## Abstract (arXiv)

In this paper, we describe the automated simulation and reporting pipeline implemented for our autonomous racing stack, ur.autopilot. The backbone of the simulation is based on a high-fidelity model of the vehicle interfaced as a Functional Mockup Unit (FMU). The pipeline can execute the software stack and the simulation up to three times faster than real-time, locally or on GitHub for Continuous Integration/- Continuous Delivery (CI/CD). As the most important input of the pipeline, there is a set of running scenarios. Each scenario allows the initialization of the ego vehicle in different initial conditions (position and speed), as well as the initialization of any other configuration of the stack. This functionality is essential to validate efficiently critical modules, like the one responsible for high-speed overtaking maneuvers or localization, which are among the most challenging aspects of autonomous racing. Moreover, we describe how we implemented a fault injection module, capable of introducing sensor delays and perturbations as well as modifying outputs of any node of the stack. Finally, we describe the design of our automated reporting process, aimed at maximizing the effectiveness of the simulation analysis.