Autonomous racing has advanced rapidly, particularly on scaled platforms, and software stacks must evolve accordingly. In this work, AROLA is introduced as a modular, layered software architecture in which fragmented and monolithic designs are reorganized into interchangeable layers and components connected through standardized ROS 2 interfaces. The autonomous-driving pipeline is decomposed into sensing, pre-processing, perception, localization and mapping, planning, behavior, control, and actuation, enabling rapid module replacement and objective benchmarking without reliance on custom message definitions. To support consistent performance evaluation, a Race Monitor framework is introduced as a lightweight system through which lap timing, trajectory quality, and computational load are logged in real time and standardized post-race analyses are generated. AROLA is validated in simulation and on hardware using the RoboRacer platform, including deployment at the 2025 RoboRacer IV25 competition. Together, AROLA and Race Monitor demonstrate that modularity, transparent interfaces, and systematic evaluation can accelerate development and improve reproducibility in scaled autonomous racing.

翻译：自动驾驶赛车技术发展迅速，尤其在缩比平台上，软件栈必须相应演进。本文提出了AROLA，一种模块化分层软件架构，它将碎片化和单体化的设计重组为可通过标准化ROS 2接口互连的、可互换的层级与组件。该自动驾驶流程被分解为传感、预处理、感知、定位与建图、规划、行为决策、控制及执行，从而实现了在不依赖自定义消息定义的情况下快速替换模块并进行目标基准测试。为支持一致的性能评估，本文引入了Race Monitor框架作为一个轻量级系统，通过该系统可实时记录圈速、轨迹质量和计算负载，并生成标准化的赛后分析报告。AROLA在仿真和基于RoboRacer平台的硬件上进行了验证，包括在2025年RoboRacer IV25赛事中的实际部署。AROLA与Race Monitor共同表明，模块化设计、透明接口和系统化评估能够加速缩比自动驾驶赛车的开发进程并提升其可复现性。