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.

翻译：本文描述了为我们的自动驾驶赛车堆栈ur.autopilot所实现的自动化仿真与报告生成流程。该仿真的核心基于一个高保真车辆模型，该模型以功能模型单元（FMU）形式进行接口封装。该流程能够在本地或GitHub上以最高三倍于实时的速度执行软件堆栈与仿真，以支持持续集成/持续交付（CI/CD）。作为流程最重要的输入，我们定义了一组运行场景。每个场景允许初始化自车在不同初始条件（位置与速度）下的状态，并可初始化堆栈的任何其他配置。此功能对于高效验证关键模块至关重要，例如负责高速超车机动或定位的模块，这些是自动驾驶赛车最具挑战性的方面之一。此外，我们描述了如何实现一个故障注入模块，该模块能够引入传感器延迟与扰动，并修改堆栈中任意节点的输出。最后，我们阐述了自动化报告流程的设计，旨在最大化仿真分析的有效性。