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硬核夜话:和一线量产专家深入聊聊自驾数据闭环工程
自动驾驶之心· 2025-08-01 16:03
Core Viewpoint - The article emphasizes the importance of a complete data closed-loop system in autonomous driving, which includes data collection, annotation, training, simulation validation, and OTA updates. As autonomous driving evolves from Level 2 to higher levels, the volume of data increases exponentially, making the breadth and depth of scenario coverage crucial for system safety [3]. Group 1: Data Closed-Loop Challenges - The data closed-loop engineering faces three core challenges: 1. The "long tail problem," which refers to the difficulty in capturing and incorporating rare but critical extreme scenarios (e.g., extreme weather, complex road conditions, sudden obstacles) into the training system [3]. 2. Data processing efficiency, as each vehicle generates terabytes of data daily due to increased sensor quantity and precision, necessitating effective filtering, annotation, and utilization of this data [3]. 3. Verification difficulties, where traditional testing methods cannot cover all possible scenarios, highlighting the need for a scientific complement between simulation testing and real-world validation [3]. Group 2: Industry Transition - The industry is transitioning from a focus on "function stacking" to "safety-centric" approaches. The challenges of data closed-loop engineering extend beyond technology to include establishing scientific verification standards, improving data processing efficiency, and balancing iteration speed with system stability to maintain a positive feedback loop in data utilization [3]. Group 3: Expert Insights - The article mentions an invitation to a data expert, Ethan, to discuss the deep challenges faced during the mass production process of autonomous driving, focusing on the essence of engineering rather than flashy technology [3].