搞自驾这七年，绝大多数的「数据闭环」都是伪闭环

Core Viewpoint - The concept of "data closed loop" in the autonomous driving industry is still largely limited to small internal loops within algorithm teams, rather than achieving the grand vision of a comprehensive system that directly solves problems through data [1]. Group 1: Definition of "True Data Closed Loop" - A "true closed loop" must meet three levels: automated problem discovery, quantifiable and reviewable solution effects, and a comprehensive trigger system that integrates real-time and historical data [4][5]. - The ideal state involves a system that can automatically classify issues, route them to the appropriate teams, and assist in developing trigger rules, thereby reducing reliance on manual processes [5]. Group 2: Current Industry Practices - Many companies' so-called "data closed loops" are more accurately described as "data-driven development processes with some automation tools," primarily limited to the perspective of individual algorithm teams [8]. - Typical workflows are often module-level and algorithm-focused, lacking a system-wide perspective [9]. Group 3: Reasons for Lack of True Closed Loops - The starting point for many companies is a "passive closed loop," where problems are identified reactively rather than through automated data analysis [10]. - Attribution of issues is often difficult, as multiple interrelated factors contribute to the same phenomenon [12]. - The data-to-solution chain often stops at data-to-model, failing to address real-world problems effectively [16]. Group 4: Data Closed Loop Practices - The company has developed a more aggressive approach to data closed loops, treating data as a product and metrics as primary citizens [24]. - The overall strategy involves quantifying real-world pain points and using triggers to convert these into actionable data [25]. Group 5: Trigger Mechanism - The trigger mechanism is designed to be lightweight and high-recall, ensuring that significant events are captured without overwhelming the system [32]. - Once a trigger is activated, it generates a micro log that is uploaded for further analysis, leading to more detailed data collection if necessary [35]. Group 6: Unified Trigger Framework - A unified trigger framework using Python allows for consistent implementation across vehicle data mining, cloud data analysis, and simulation validation [50]. - This framework enables non-technical team members to participate in writing rules, thus democratizing the process of data analysis [54]. Group 7: Distinction Between World Labels and Algorithm Labels - The company maintains two types of labels: world-level labels that describe objective physical conditions and model-level labels that depend on algorithm performance [61]. - This distinction is crucial for effective data analysis and problem-solving in the autonomous driving context [61]. Group 8: Use of Generative and Simulation Data - Generative data is primarily used to address long-tail scenarios that are difficult to encounter in real life, but real data remains essential for evaluation and validation [67]. - The company emphasizes the importance of filtering data through structured labels before applying vector retrieval methods to ensure efficiency and accuracy [64].