近处感知、远处推理：面向自动驾驶的连贯长时序轨迹预测
Perceiving the Near, Reasoning the Distant: Coherent Long-Horizon Trajectory Prediction for Autonomous Driving
将长时序轨迹预测按近端/远端解耦为两条专用解码通路，并用运动学感知的一致性损失约束航向演化，这一「分阶段解码 + 运动学先验损失」思路可直接迁移到行人轨迹预测、视频中物体未来运动预测等需要长时序物理合理性的任务。
原文依据 we introduce a novel motion-aware coherence loss that explicitly embeds kinematic priors to enforce trajectory consistency
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论可靠的长时序轨迹预测既需要高位置精度，也需要物理上合理的时序运动一致性。然而，现有方法存在两个根本性局限。第一，它们忽视了预测逻辑的内在差异：近未来轨迹主要由历史动力学决定，而远未来行为由高层语义上下文驱动。但大多数方法采用统一的解码通路，模糊了这种时序区分。第二，尽管近未来相对更易预测，现有方法缺乏跨时间范围连贯传播轨迹的机制，常导致运动学上不合理的预测，表现为航向演化不一致以及长时序性能下降。为应对这些挑战，我们提出 NDPNet，一种双阶段架构，将近端与远端建模解耦为专用通路，并配备专门的过渡模块以保证平滑的时序衔接。此外，我们引入一种新的运动感知一致性损失，显式嵌入运动学先验以强制轨迹一致性。大量实验表明，NDPNet 在 Argoverse 2 和 WOMD 上取得 SOTA 性能。值得注意的是，在 WOMD 上，它在所有标准时间范围（3s、5s、8s）的 minFDE_6 与 minADE_6 两项指标上均排名第一，且未使用集成学习或 NMS 后处理，并首次在 8s 预测上实现低于 1.75 的 minFDE_6，大幅超越此前方法。代码将随后发布。
英文原文摘要 1555 字符
Reliable long-horizon trajectory prediction requires both high positional accuracy and physically plausible temporal motion consistency. However, existing methods suffer from two fundamental limitations. First, they overlook the inherent difference in prediction logic: near-future trajectories are primarily governed by historical dynamics, whereas distant-future behaviors are driven by high-level semantic context. Yet, most methods employ a unified decoding pathway that blurs the temporal distinction.Second, although the near future is relatively easier to predict, existing methods lack mechanisms for coherent trajectory propagation across time horizons, often resulting in kinematically implausible predictions with inconsistent heading evolution and degraded long-horizon performance. To address these challenges, we propose NDPNet, a dual-stage architecture that decouples near- and distant-horizon modeling into specialized pathways, with a dedicated transition module ensuring smooth temporal bridging. Furthermore, we introduce a novel motion-aware coherence loss that explicitly embeds kinematic priors to enforce trajectory consistency. Extensive experiments show that NDPNet achieves SOTA performance on Argoverse 2 and WOMD. Notably, on WOMD, it ranks 1^ \text st in both minFDE _6 and minADE _6 across all standard horizons (3s, 5s, 8s) without ensemble learning or NMS post-processing, and is the first to achieve sub-1.75 minFDE _6 for 8s prediction, surpassing prior methods by a large margin. The code will be released subsequently.