标题： 运动中的远见：通过奖励启发式方法加强轨迹预测 显示英文标题

标题： Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics

摘要： 道路交通代理的运动预测对于确保自动驾驶系统的安全性既是一个重大挑战，也是一个关键需求。 与大多数直接预测未来轨迹的数据驱动方法不同，我们从规划的角度重新思考这一任务，倡导一种“先推理，后预测”的策略，将行为意图作为轨迹预测的空间指导。 为了实现这一点，我们引入了一个可解释的、基于奖励的意图推理器，该推理器建立在一种新颖的以查询为中心的逆强化学习（IRL）方案之上。 我们的方法首先将交通代理和场景元素编码为统一的向量化表示，然后通过以查询为中心的范式聚合上下文特征。 这使得能够通过IRL推导出一个奖励分布，这是在给定场景上下文中目标代理行为的紧凑而有信息的表示。 在这一奖励启发式的指导下，我们进行策略展开以推理多个可能的意图，为后续的轨迹生成提供有价值的先验知识。 最后，我们开发了一个层次化的DETR类似解码器，结合了双向选择状态空间模型，以生成准确的未来轨迹及其相关概率。 在大规模Argoverse和nuScenes运动预测数据集上的大量实验表明，我们的方法显著提高了轨迹预测的置信度，相对于最先进的方法取得了高度具有竞争力的性能。 显示英文摘要

摘要： Motion forecasting for on-road traffic agents presents both a significant challenge and a critical necessity for ensuring safety in autonomous driving systems. In contrast to most existing data-driven approaches that directly predict future trajectories, we rethink this task from a planning perspective, advocating a "First Reasoning, Then Forecasting" strategy that explicitly incorporates behavior intentions as spatial guidance for trajectory prediction. To achieve this, we introduce an interpretable, reward-driven intention reasoner grounded in a novel query-centric Inverse Reinforcement Learning (IRL) scheme. Our method first encodes traffic agents and scene elements into a unified vectorized representation, then aggregates contextual features through a query-centric paradigm. This enables the derivation of a reward distribution, a compact yet informative representation of the target agent's behavior within the given scene context via IRL. Guided by this reward heuristic, we perform policy rollouts to reason about multiple plausible intentions, providing valuable priors for subsequent trajectory generation. Finally, we develop a hierarchical DETR-like decoder integrated with bidirectional selective state space models to produce accurate future trajectories along with their associated probabilities. Extensive experiments on the large-scale Argoverse and nuScenes motion forecasting datasets demonstrate that our approach significantly enhances trajectory prediction confidence, achieving highly competitive performance relative to state-of-the-art methods.