Long-tail motion forecasting is a core challenge for autonomous driving, where rare yet safety-critical events-such as abrupt maneuvers and dense multi-agent interactions-dominate real-world risk. Existing approaches struggle in these scenarios because they rely on either non-interpretable clustering or model-dependent error heuristics, providing neither a differentiable notion of "tailness" nor a mechanism for rapid adaptation. We propose SAML, a Semantic-Aware Meta-Learning framework that introduces the first differentiable definition of tailness for motion forecasting. SAML quantifies motion rarity via semantically meaningful intrinsic (kinematic, geometric, temporal) and interactive (local and global risk) properties, which are fused by a Bayesian Tail Perceiver into a continuous, uncertainty-aware Tail Index. This Tail Index drives a meta-memory adaptation module that couples a dynamic prototype memory with an MAML-based cognitive set mechanism, enabling fast adaptation to rare or evolving patterns. Experiments on nuScenes, NGSIM, and HighD show that SAML achieves state-of-the-art overall accuracy and substantial gains on top 1-5% worst-case events, while maintaining high efficiency. Our findings highlight semantic meta-learning as a pathway toward robust and safety-critical motion forecasting.

翻译：长尾运动预测是自动驾驶领域的核心挑战，其中罕见但安全关键的事件——如突发机动和密集多智能体交互——主导了现实世界的风险。现有方法在这些场景中表现不佳，因为它们依赖于不可解释的聚类或模型依赖的误差启发式方法，既未提供“尾部性”的可微分定义，也缺乏快速适应机制。我们提出了SAML，一种语义感知元学习框架，首次为运动预测引入了可微分的尾部性定义。SAML通过语义上有意义的内在属性（运动学、几何学、时间性）和交互属性（局部与全局风险）量化运动稀有性，这些属性由贝叶斯尾部感知器融合为连续、不确定性感知的尾部指数。该尾部指数驱动元记忆适应模块，将动态原型记忆与基于MAML的认知集合机制相结合，实现对罕见或演化模式的快速适应。在nuScenes、NGSIM和HighD数据集上的实验表明，SAML实现了最先进的整体精度，并在前1-5%最差事件上取得显著提升，同时保持高效率。我们的研究结果凸显了语义元学习作为实现鲁棒且安全关键的运动预测的有效途径。