针对自动驾驶罕见场景，提出新评估与增强方法提升轨迹预测鲁棒性。
LongComp: Long-Tail Compositional Zero-Shot Generalization for Robust Trajectory Prediction
- 用场景分解框架分离车辆与社会上下文，构造罕见组合测试集。
- 在封闭与开放世界设置下，使模型性能下降分别达5.0%和14.7%。
- 通过门控网络与难度预测头，将性能差距缩小至2.8%和11.5%。
自动驾驶轨迹预测方法必须应对稀有且高危的场景，仅依赖真实数据采集难以满足需求。为此，我们提出新的长尾评估设置，通过重新划分数据集生成具有挑战性的分布外（OOD）测试集。首先引入安全导向的场景因子分解框架，将场景拆分为离散的自车与社会上下文。借鉴计算机视觉中的组合零样本图像标注思路，通过保留新颖的上下文组合，构建封闭世界与开放世界测试环境。该过程导致先进基线模型在未来运动预测上的性能分别下降5.0%（封闭世界）和14.7%（开放世界）。为提升泛化能力，我们将任务模块化门控网络引入轨迹预测模型，并设计辅助难度预测头以优化内部表示。所提策略联合将两个设置下的性能差距降至2.8%和11.5%，同时保持对分布内性能的提升。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。