特斯拉选择“端到端框架”的核心原因:模块化框架难以编码人类价值观、感知与预测规划之间的接口定义会带来信息传递损失、端到端能应对现实世界的长尾问题/同构计算带来的确定性延迟,端到端也能更好地契合AI 领域的Scaling-law。端到端面临的主要挑战以及特斯拉如何解决:维度灾难(特斯拉采用复杂的触发机制来回传长尾场景数据)、怎么保证模型的可解释性与安全性(允许模型输出多样的中间结果(例如占用、其它...
Source Link特斯拉选择“端到端框架”的核心原因:模块化框架难以编码人类价值观、感知与预测规划之间的接口定义会带来信息传递损失、端到端能应对现实世界的长尾问题/同构计算带来的确定性延迟,端到端也能更好地契合AI 领域的Scaling-law。端到端面临的主要挑战以及特斯拉如何解决:维度灾难(特斯拉采用复杂的触发机制来回传长尾场景数据)、怎么保证模型的可解释性与安全性(允许模型输出多样的中间结果(例如占用、其它...
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