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Research & IP研究与专利

Tesla Patent Filing Proposes Synthetic Data to Train Self-Driving AI特斯拉专利申请提出用合成数据训练自动驾驶AI

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Tesla filed a patent application titled "data synthesis for autonomous control systems," proposing synthetic data to more thoroughly train its autonomous driving AI models, per a November 4, 2024 report by The Daily Upside's Nat Rubio-Licht.
The filing states model performance typically improves with more training data, but real-world data collection is "costly and time-consuming," and describes two synthetic-data methods, the first of which modifies authentic sensor data gathered in real-world simulations (e.g., altering conditions).
The filing arrives as Tesla doubles down on autonomy through its robotaxi push, underscoring synthetic data's role in scaling training for its self-driving ambitions.

据The Daily Upside记者Nat Rubio-Licht于2024年11月4日报道，特斯拉提交了名为"data synthesis for autonomous control systems"（自动控制系统数据合成）的专利申请，提出用合成数据更充分地训练其自动驾驶AI模型。
该专利文件指出，模型性能通常随训练数据量增加而提升，但现实世界数据采集"成本高、耗时长"，专利提出两种合成数据生成方法，其中第一种是修改现实模拟中采集的真实传感器数据（如改变路况等条件）。
该专利申请正值特斯拉通过机器人出租车业务加倍押注自动驾驶之际，凸显合成数据对扩大自动驾驶训练规模的作用。

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