用雷达点云增强远距离目标追踪，恶劣天气下效果显著提升
Radar-Informed 3D Multi-Object Tracking under Adverse Conditions
- 将雷达点云作为显式观测输入，改进状态估计
- 长距离追踪AMOTA提升12.7%，恶劣天气下提升10.3%
- 适合自动驾驶中复杂环境下的多目标跟踪任务
3D多目标跟踪在真实场景中面临恶劣条件和远距离下一致性差的挑战。现有传感器融合方法通常将雷达视为网络中的一个可学习特征，当模型性能下降时，雷达的鲁棒性优势也随之减弱。本文提出RadarMOT框架，显式利用雷达点云作为额外观测，用于修正状态估计并恢复远距离被检测器遗漏的目标。在MAN-TruckScenes数据集上的实验表明，RadarMOT在长距离下使平均多目标追踪准确率（AMOTA）提升12.7%，在恶劣天气下最高提升10.3%。代码将开源于https://github.com/bingxue-xu/radarmot。
原文摘要 · Abstract (English)
The challenge of 3D multi-object tracking is achieving robustness in real-world applications, for example under adverse conditions and maintaining consistency as distance increases. To overcome these challenges, sensor fusion approaches that combine LiDAR, cameras, and radar have emerged. However, existing multimodal methods usually treat radar as another learned feature inside the network. When the overall model degrades in difficult environments, the robustness advantages that radar could provide are also reduced. In this paper we propose RadarMOT, a radar-informed 3D multi-object tracking framework that explicitly uses radar point clouds as additional observations to refine state estimation and recover objects missed by the detector at long ranges. Evaluations on the MAN-TruckScenes dataset show that RadarMOT consistently improves the Average Multi-Object Tracking Accuracy (AMOTA) by 12.7\% at long range and up to 10.3\% in adverse weather. The code will be available at https://github.com/bingxue-xu/radarmot
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