用4D雷达和贝叶斯方法提升恶劣天气下的多目标追踪精度
Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar
- 用Transformer网络捕捉复杂运动轨迹，替代传统线性模型
- 在K-Radar数据集上AMOTA提升5.7%，尤其在恶劣天气下表现更优
- 适合自动驾驶系统在雨雪等复杂环境中的感知需求
高精度3D多目标跟踪对自动驾驶至关重要，但激光雷达和摄像头在恶劣天气下性能下降。基于雷达的方案虽具备鲁棒性，却常受限于垂直分辨率低和运动模型简单。现有基于卡尔曼滤波的方法依赖固定噪声协方差，难以适应突然变道等机动行为。本文提出贝叶斯-4DRTrack，一种基于4D雷达的多目标跟踪框架，采用Transformer驱动的运动预测网络以捕捉非线性运动动态，并在检测与预测阶段均引入贝叶斯近似。此外，两阶段数据关联利用多普勒信息更好区分近距离目标。在包含恶劣天气场景的K-Radar数据集上评估，该方法相比传统运动模型与固定噪声协方差的方法，平均多目标跟踪准确率（AMOTA）提升5.7%。结果表明，在真实复杂条件下具备更强鲁棒性和准确性。
原文摘要 · Abstract (English)
Accurate 3D multi-object tracking (MOT) is vital for autonomous vehicles, yet LiDAR and camera-based methods degrade in adverse weather. Meanwhile, Radar-based solutions remain robust but often suffer from limited vertical resolution and simplistic motion models. Existing Kalman filter-based approaches also rely on fixed noise covariance, hampering adaptability when objects make sudden maneuvers. We propose Bayes-4DRTrack, a 4D Radar-based MOT framework that adopts a transformer-based motion prediction network to capture nonlinear motion dynamics and employs Bayesian approximation in both detection and prediction steps. Moreover, our two-stage data association leverages Doppler measurements to better distinguish closely spaced targets. Evaluated on the K-Radar dataset (including adverse weather scenarios), Bayes-4DRTrack demonstrates a 5.7% gain in Average Multi-Object Tracking Accuracy (AMOTA) over methods with traditional motion models and fixed noise covariance. These results showcase enhanced robustness and accuracy in demanding, real-world conditions.
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