Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar

• URL: http://arxiv.org/abs/2502.01357v1
• Date: Mon, 03 Feb 2025 13:49:21 GMT
• Title: Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar
• Authors: Dong-In Kim, Dong-Hee Paek, Seung-Hyun Song, Seung-Hyun Kong,
• Abstract summary: 3D multi-object tracking (MOT) is vital for autonomous vehicles, yet LiDAR and camera-based methods degrade in adverse weather. We propose Bayes-4DRTrack, a 4D Radar-based MOT framework that adopts a transformer-based motion prediction network.
• Score: 13.438311878715536
• License:
• Abstract: 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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