L4数据闭环：三端统一Trigger框架，让异常事件自动长成问题单

Core Viewpoint - The article discusses the implementation of a unified Trigger framework for automatic detection, attribution, and management of anomalies in autonomous driving systems, transitioning from manual log analysis to automated problem identification and classification [2][5][69]. Group 1: Transition from Manual to Automated Processes - The traditional method of bug detection in autonomous driving relies heavily on experienced personnel and separate logic for cloud, vehicle, and simulation, making it difficult to systematically identify and prioritize issues [3][4]. - The goal is to enable anomalies to be automatically identified and structured into problem samples without human intervention, leading to a more efficient problem management system [5][6]. Group 2: Definition and Functionality of Trigger - The Trigger framework is defined as a combination of feature engineering and tokenization, where raw logs are transformed into structured tokens for classification [7][8]. - The framework aims to unify the logic across vehicle, cloud, and simulation environments, ensuring consistent definitions of events and problems [10][15]. Group 3: Trigger Framework Design - The Trigger framework is designed with three layers: Trigger definition, Trigger runtime, and Trigger management, allowing for a standardized execution interface across platforms [16][19]. - Each Trigger has a unique identifier and metadata, including dependencies and output labels, facilitating its integration into various systems [19][20]. Group 4: Case Development from Anomalies - Anomalies detected by the Trigger lead to the creation of structured cases, which are further analyzed using historical data to provide evidence and insights [40][41]. - The process involves breaking down a road case into multiple bad cases based on module or issue classification, allowing for targeted problem resolution [41][42]. Group 5: Classification and Automation - The classification of issues has evolved from rule-based systems to utilizing LLMs (Large Language Models) for more nuanced categorization based on token sequences generated by the Trigger [46][48]. - The automation of ticket generation and regression testing is integrated into the workflow, reducing manual effort and improving response times for identified issues [52][54]. Group 6: Continuous Improvement and Feedback Loop - The system incorporates a feedback loop where modifications by developers on classified cases provide supervision signals to improve the classification accuracy over time [67][70]. - The framework supports the identification of head problems through clustering and analysis of case similarities, enhancing the overall problem management process [68][72].