Open-Source Autonomous Driving System Analysis and Multi-Disciplinary Hardware-in-the-Loop Research Paradigm with Reinforcement-Learning Testing and Large Language Models
2026-08-31 • Software Engineering
Software EngineeringRobotics
AI summaryⓘ
The authors focus on improving the way experiments with multiple autonomous vehicles are recorded and reviewed. They created a system that unifies different parts of testing—like software changes, vehicle tests, and feedback—so everything can be checked together easily. They also use AI tools to help organize data and detect unusual behaviors. Their approach helps researchers better understand how experiments relate to real driving conditions and makes collaboration smoother.
Autonomous drivingExperimental frameworkMulti-vehicle collaborationSoftware-hardware testingApollo autonomous systemHongqi EVLarge language modelsReinforcement learningScenario generationCode reuse
Authors
Dianjing Cheng, Yike Li, Lan Yang, Shan Fang, Wenjia Niu, Xiangyu Shi, Xinyi Zhao, Yunzhe Tian, XingYu Wu, Xiaoshu Cui, Yuanwan Chen, Jialu Sun, Zhongli Wang, Biao Liu, Jiaqi Yang, Jinghui Feng, Feifei Su, Juan Du, Shuangde Fang, Yi Qian, Huiyun Li, Yuansheng Liu, Peng Sun, Mingming Wan, Nan Chen, Ruipeng Gao
Abstract
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.