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 a "true data closed loop" in the autonomous driving industry is still far from realization, with most current implementations being limited to small, internal loops within individual algorithm teams rather than the comprehensive systems envisioned in early presentations [1]. Group 1: Definition of a True Data Closed Loop - A true data closed loop should automate problem discovery, allowing systems to identify anomalies from vast operational data without relying on manual feedback [4]. - The effectiveness of solutions must be quantifiable and reviewable, requiring a comprehensive trigger system that integrates real-time and historical data analysis [5]. - The system should continuously assess whether the investments in data, computing power, and development yield satisfactory results [5]. Group 2: Current Industry Practices - Many companies currently operate under a "data-driven development process with some automation tools," which are often limited to the perspectives of individual algorithm teams [8]. - Typical workflows are more about module-level, algorithmic closed loops rather than a holistic system-level approach [9]. Group 3: Challenges in Achieving True Data Closed Loops - Many existing systems are reactive rather than proactive, relying on manual identification of issues rather than automated detection [10]. - Attribution of problems is often difficult, as multiple interrelated factors contribute to issues, making it hard to pinpoint the source of a problem [12]. - The transition from data to actionable solutions often halts at the model training stage, lacking a clear connection to real-world problems [16]. - The degree of "self-healing" in current systems is limited, with many platforms resembling automated production lines rather than self-correcting systems [17]. - Organizational structures often fragment the closed loop, leading to communication issues between teams [18]. Group 4: Practical Implementation of Data Closed Loops - The company has developed a more aggressive approach to data closed loops, treating data as a product and metrics as primary citizens [24]. - The methodology emphasizes quantifying real-world pain points and ensuring all critical incidents are recorded accurately [26]. - A micro log and mini log mechanism is employed to capture high-recall, low-overhead data from vehicles, focusing on significant driving events [30]. - The system allows for dynamic control of data mining tasks based on real-time needs, ensuring flexibility in data collection [59]. Group 5: Distinction Between World Labels and Algorithm Labels - The company maintains two types of labels: world-level labels that describe the physical environment and model-level labels that reflect algorithm performance [61]. - This distinction is crucial for effective data analysis and problem-solving, ensuring that the focus remains on real-world scenarios rather than solely on algorithmic outputs [61]. Group 6: Use of Generative and Simulation Data - Generative data is utilized to address long-tail scenarios that are difficult to encounter in reality, but it is not a substitute for real-world evaluation [67]. - The company emphasizes that while recall rates may improve with generative data, the potential for increased false positives must be carefully monitored [70].