Methods for trajectory prediction in Autonomous Driving must contend with rare, safety-critical scenarios that make reliance on real-world data collection alone infeasible. To assess robustness under such conditions, we propose new long-tail evaluation settings that repartition datasets to create challenging out-of-distribution (OOD) test sets. We first introduce a safety-informed scenario factorization framework, which disentangles scenarios into discrete ego and social contexts. Building on analogies to compositional zero-shot image-labeling in Computer Vision, we then hold out novel context combinations to construct challenging closed-world and open-world settings. This process induces OOD performance gaps in future motion prediction of 5.0% and 14.7% in closed-world and open-world settings, respectively, relative to in-distribution performance for a state-of-the-art baseline. To improve generalization, we extend task-modular gating networks to operate within trajectory prediction models, and develop an auxiliary, difficulty-prediction head to refine internal representations. Our strategies jointly reduce the OOD performance gaps to 2.8% and 11.5% in the two settings, respectively, while still improving in-distribution performance.

翻译：自动驾驶中的轨迹预测方法必须应对罕见且安全关键的情景，仅依赖真实世界数据收集是不现实的。为评估此类条件下的鲁棒性，我们提出新的长尾评估设置，通过重新划分数据集构建具有挑战性的分布外（OOD）测试集。首先，我们引入一种安全导向的情景因子化框架，将情景解耦为离散的自车与社会上下文。借鉴计算机视觉中组合零样本图像标注的类比，我们随后保留新颖的上下文组合，构建封闭世界与开放世界两种挑战性设置。该过程导致先进基线模型在轨迹预测中，封闭世界与开放世界设置下的分布外性能差距分别达到5.0%与14.7%（相对于分布内性能）。为提升泛化能力，我们将任务模块化门控网络扩展至轨迹预测模型内部，并开发辅助的难度预测头以优化内部表征。我们的策略联合将两种设置下的分布外性能差距分别降低至2.8%与11.5%，同时仍提升了分布内性能。