解决多车感知中因延迟导致的特征错位问题，实现时间对齐的协同感知。
TraF-Align: Trajectory-aware Feature Alignment for Asynchronous Multi-agent Perception
- 通过预测物体特征轨迹，生成时序采样点来对齐历史与当前特征。
- 在真实数据集上显著提升异步协作感知性能，刷新基准线。
- 适合需要跨车辆实时协同感知的自动驾驶系统应用。
协同感知极大提升了单个车辆的感知能力，但跨车辆延迟仍是关键挑战。延迟导致空间与语义特征错位，使得本车实时观测与他人延迟数据难以融合。为此，我们提出TraF-Align框架，通过预测从过去观测到本车当前时刻的物体特征轨迹，生成沿轨迹的时序采样点，引导当前时刻查询关注相关历史特征，支持当前特征重建并促进多帧间语义交互。该方法有效纠正空间错位，确保跨代理语义一致性，补偿运动差异，实现一致的特征融合。在两个真实世界数据集V2V4Real和DAIR-V2X-Seq上的实验表明，TraF-Align为异步协同感知设立了新基准。
原文摘要 · Abstract (English)
Cooperative perception presents significant potential for enhancing the sensing capabilities of individual vehicles, however, inter-agent latency remains a critical challenge. Latencies cause misalignments in both spatial and semantic features, complicating the fusion of real-time observations from the ego vehicle with delayed data from others. To address these issues, we propose TraF-Align, a novel framework that learns the flow path of features by predicting the feature-level trajectory of objects from past observations up to the ego vehicle's current time. By generating temporally ordered sampling points along these paths, TraF-Align directs attention from the current-time query to relevant historical features along each trajectory, supporting the reconstruction of current-time features and promoting semantic interaction across multiple frames. This approach corrects spatial misalignment and ensures semantic consistency across agents, effectively compensating for motion and achieving coherent feature fusion. Experiments on two real-world datasets, V2V4Real and DAIR-V2X-Seq, show that TraF-Align sets a new benchmark for asynchronous cooperative perception.
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