发表机构
Northeastern University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对社会轨迹预测中动态编码未必带来动态激活的问题，提出GEAR模型，通过偏置分解与逐未来步骤动态激活个体运动与社会共振偏置，在ETH-UCY、SDD和NBA上超越基线并达到最先进性能。
AI 中文摘要
人类轨迹预测需要对个体的运动模式以及智能体之间的社会交互进行建模。现有方法通过使用注意力机制、图结构和时间编码器来捕获动态社会上下文，已经取得了实质性进展。然而，大多数方法主要关注社会信息如何被编码，而对于编码后的社会上下文在未来的轨迹生成过程中应如何发挥作用，却较少给予明确的关注。在本文中，我们认为动态的社会编码并不一定意味着动态的社会激活。相同的交互上下文在不同的未来时间范围和场景密度下可能需要不同的激活强度：当交互证据强时，社会线索应被加强，而当交互证据弱或存在噪声时，社会线索应被抑制。为了解决这个问题，我们提出了GEAR，一种用于人类轨迹预测的生成感知偏置激活模型。基于偏置分解的轨迹生成公式，GEAR在最终轨迹组合之前，在每个未来步骤动态激活个体运动和社会共振偏置项。这使得模型能够明确控制个体和社会偏置组件在生成过程中何时以及以何种强度参与。在ETH-UCY、SDD和NBA上的实验表明，GEAR持续改进了基于共振的基线方法，并达到了具有竞争力的最先进性能。对激活模式和密度分组误差的进一步分析验证了在轨迹生成过程中校准编码社会上下文的重要性。我们的代码可在以下网址获取：此https URL。
英文摘要
Human trajectory prediction requires modeling both individual motion patterns and social interactions among agents. Existing methods have made substantial progress by using attention mechanisms, graph structures, and temporal encoders to capture dynamic social context. However, most of them primarily focus on how social information is encoded, while paying less explicit attention to how the encoded social context should take effect during future trajectory generation. In this paper, we argue that dynamic social encoding does not necessarily imply dynamic social activation. The same interaction context may require different activation strengths across future horizons and scene densities: social cues should be strengthened when interaction evidence is strong, but suppressed when they are weak or noisy. To address this issue, we propose GEAR, a generation-aware bias activation model for human trajectory prediction. Built upon a bias-decomposed trajectory generation formulation, GEAR dynamically activates the individual-motion and social-resonance bias terms at each future step before final trajectory composition. This allows the model to explicitly control when and how strongly individual and social bias components participate in generation. Experiments on ETH-UCY, SDD, and NBA show that GEAR consistently improves the resonance-based baseline and achieves competitive state-of-the-art performance. Further analyses of activation patterns and density-grouped errors validate the importance of calibrating encoded social context during trajectory generation. Our code is available at https://github.com/11isnotavailable/GEAR.git.
CommentsAccepted by ICDM 2026
