通过融合外观与运动信息，提升自动驾驶中目标检测与轨迹预测的准确性。
MASAR: Motion-Appearance Synergy Refinement for Joint Detection and Trajectory Forecasting
- 采用时空联合编码机制，同步学习目标外观与运动特征。
- 在nuScenes数据集上，minADE和minFDE指标提升超20%。
- 适合关注端到端感知与预测协同优化的研究者。
传统自动驾驶系统通过手工设计的边界框接口连接感知与预测模块，限制了信息流动并导致误差传播。近期研究尝试构建端到端模型联合处理感知与预测，但常未能充分挖掘外观与运动线索的协同效应，主要依赖短期视觉特征。本文提出MASAR，一种全可微分的3D检测与轨迹预测联合框架，兼容任意基于Transformer的3D检测器。MASAR采用以物体为中心的时空机制，联合编码外观与运动特征。通过预测历史轨迹并利用外观线索进行修正，捕捉长期时序依赖，从而提升未来轨迹预测性能。在nuScenes数据集上的实验表明，MASAR在保持鲁棒检测性能的同时，minADE与minFDE指标提升超过20%。代码与模型已开源：https://github.com/aminmed/MASAR。
原文摘要 · Abstract (English)
Classical autonomous driving systems connect perception and prediction modules via hand-crafted bounding-box interfaces, limiting information flow and propagating errors to downstream tasks. Recent research aims to develop end-to-end models that jointly address perception and prediction; however, they often fail to fully exploit the synergy between appearance and motion cues, relying mainly on short-term visual features. We follow the idea of "looking backward to look forward", and propose MASAR, a novel fully differentiable framework for joint 3D detection and trajectory forecasting compatible with any transformer-based 3D detector. MASAR employs an object-centric spatio-temporal mechanism that jointly encodes appearance and motion features. By predicting past trajectories and refining them using guidance from appearance cues, MASAR captures long-term temporal dependencies that enhance future trajectory forecasting. Experiments conducted on the nuScenes dataset demonstrate MASAR's effectiveness, showing improvements of over 20% in minADE and minFDE while maintaining robust detection performance. Code and models are available at https://github.com/aminmed/MASAR.
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