构建模块化架构，让无人赛车开发更高效可复现。
AROLA: A Modular Layered Architecture for Scaled Autonomous Racing
- 将自动驾驶流程拆分为感知、规划、控制等标准层，通过ROS 2接口连接。
- 实测在仿真与硬件上均支持快速模块替换和实时性能监控。
- 适合追求可复现性与协作开发的无人赛车研究者使用。
自主赛车技术快速发展，尤其在小型化平台。本文提出AROLA，一种模块化分层软件架构，将原本零散或臃肿的设计重构为可通过标准化ROS 2接口互联的可替换组件。自动驾驶流程被分解为感知、预处理、定位与建图、规划、行为决策、控制及执行等层，实现模块快速替换与客观评测，无需依赖自定义消息格式。为支持一致性能评估，引入轻量级Race Monitor框架，实时记录单圈用时、轨迹质量与计算负载，并生成标准化赛后分析报告。AROLA在仿真环境与RoboRacer硬件平台上验证，包括2025年RoboRacer IV25竞赛部署。结果表明，模块化设计、透明接口与系统化评估能显著加速开发进程并提升可复现性。
原文摘要 · Abstract (English)
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.
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