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

TL;DR

This paper presents a high-fidelity, fast simulation and automated reporting pipeline for autonomous racing, enabling efficient validation of critical modules and fault injection for robustness testing.

Contribution

The paper introduces a novel automated simulation pipeline with high-speed execution, scenario initialization, fault injection, and reporting tailored for autonomous racing systems.

Findings

Simulation runs up to three times faster than real-time.

Supports diverse scenario initialization for comprehensive testing.

Includes fault injection capabilities for robustness evaluation.

Abstract

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.