发表机构
School of Cyberspace Science and Technology, Beijing Jiaotong University; School of Information Engineering, Chang’an University（北京交通大学网络空间科学与技术学院; 长安大学信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对开源自动驾驶系统实验记录分散问题，提出Apollo-on-Hongqi EV环境下的实车实验框架，结合大语言模型与强化学习测试，为相关研究提供可审查的实验基础。
AI 中文摘要
开源自动驾驶系统为智能车辆研究提供了可检查的软件基础。在实车部署条件下，实验条件的记录与审查对于解释系统行为及复用实验结果至关重要。然而，在涉及多车辆的共享实车环境中，任务流程、代码修改及硬件测试反馈常分散于不同团队与实验阶段，难以维持连续且可审查的实验记录。为解决此局限，本文研究Apollo-on-Hongqi EV环境，提出一种实车实验框架，该框架将多车辆实验、基于代码仓库的代码复用及软硬件测试反馈整合至统一审查流程中。大语言模型与基于强化学习（RL）的测试作为辅助组件，用于记录组织、异常总结及基于仿真的候选场景生成。基于此设置，本文分析了多车辆协同实验、代码与实验技能共享、软硬件协同测试的初步证据，分析表明实验记录可与其运行条件一同被审查，为Apollo-on-Hongqi EV研究提供了可审查的基础。
英文摘要
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.
Comments33 pages, 7 figures, 7 tables
