构建可复现的自动驾驶实车测试框架，融合代码与硬件协同记录。
Open-Source Autonomous Driving System Analysis and Multi-Disciplinary Hardware-in-the-Loop Research Paradigm with Reinforcement-Learning Testing and Large Language Models
- 统一多车实验、代码复用与软硬件反馈流程，支持全程可追溯。
- 利用大模型与强化学习生成测试场景，提升异常检测与记录效率。
- 适合自动驾驶团队共享实验数据与协作开发，提升研究可复现性。
开源自动驾驶系统为智能汽车研究提供了可检查的软件基础。在真实车辆部署条件下，实验环境的记录与复盘对理解系统行为和重复实验结果至关重要。然而，在涉及多辆车、多任务流程、代码修改与硬件测试反馈的共享实车环境中，这些信息常分散于不同团队和实验阶段，难以维持连续且可审查的实验记录。为此，本文以Apollo-on-Hongqi EV为案例，提出一个实车实验框架，将多车实验、基于仓库的代码复用与软硬件测试反馈整合进统一的可审查流程。大语言模型与基于强化学习的测试作为辅助组件，用于记录组织、异常总结及仿真场景生成。基于此框架，本文分析了多车协同实验、代码与实验技能共享、软硬件协同测试的初步证据，表明实验记录可与其运行条件一同被审查，为Apollo-on-Hongqi EV研究提供了可复现的基础。
原文摘要 · Abstract (English)
Open-source autonomous driving systems provide an inspectable software foundation for intelligent vehicle research. Under real-vehicle deployment conditions, the recording and review of experimental conditions are important for interpreting system behavior and reusing experimental results. However, in a shared real-vehicle environment involving multiple vehicles, task processes, code modifications, and hardware testing feedback are often distributed across different teams and experimental stages, making it challenging to maintain continuous and reviewable experimental records. To address this limitation, this paper examines an Apollo-on-Hongqi EV environment and proposes a real-vehicle experimental framework. The framework connects multi-vehicle experiments, repository-based code reuse and software-hardware testing feedback within a unified review process. Large language models and RL-based testing serve as auxiliary components for record organization, anomaly summarization, and simulation-based candidate scenario generation. Based on this setting, this paper analyzes preliminary evidence from multi-vehicle collaborative experimentation, code and experimental-skill sharing, and software-hardware collaborative testing. The analysis shows that experimental records can be examined together with their operating conditions, providing a reviewable basis for Apollo-on-Hongqi EV research.
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