In a recent public interview, Elon Musk once again emphasized the critical role of training data in achieving full self-driving (FSD) capability. He pointed out that Tesla’s autonomous driving system does not rely on high-definition maps or LiDAR, but instead uses a pure vision-based approach (Tesla Vision) trained on massive amounts of real-world driving data. According to Musk, the scale and diversity of data are essential for improving neural network performance—only by collecting sufficient video footage and driving behavior data across various complex road conditions, weather scenarios, and traffic situations can AI models learn to make safe, human-like decisions.He also noted that Tesla’s global fleet of millions of camera-equipped vehicles continuously contributes new training samples every day. This ‘shadow mode’ enables constant learning and rapid iteration of the FSD system. Musk even stated, “The race for autonomy is fundamentally a race for data.” As more users activate FSD features, Tesla will build an increasingly robust data feedback loop, accelerating progress toward Level 4 or even Level 5 autonomy. However, he acknowledged that while data is crucial, efficient algorithmic architecture and engineering implementation remain equally vital.
在近期的一次公开访谈中，埃隆·马斯克再次强调了训练数据对实现完全自动驾驶（FSD）的关键作用。他指出，特斯拉的自动驾驶系统并非依赖高精地图或激光雷达，而是通过纯视觉方案（Tesla Vision）结合海量真实驾驶数据进行训练。马斯克认为，数据规模和多样性是提升神经网络性能的核心——只有在各种复杂路况、天气条件和交通场景下收集足够多的视频与驾驶行为数据，AI模型才能学会像人类一样做出安全、合理的决策。他还提到，特斯拉拥有全球数百万辆搭载摄像头的车辆，每天都在为系统贡献新的训练样本，这种“影子模式”下的持续学习机制，使FSD能够快速迭代优化。马斯克甚至表示：“自动驾驶的竞争本质上是数据的竞争。”未来，随着更多用户启用FSD功能，特斯拉将积累更高质量的数据闭环，从而加速向L4甚至L5级别自动驾驶迈进。不过，他也承认，数据虽重要，但高效的算法架构和工程实现同样不可或缺。
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