Simpler Is Better: Revisiting Doppler Velocity for Enhanced Moving Object Tracking with FMCW LiDAR

Abstract

Real-time and accurate perception of dynamic objects is crucial for autonomous driving. To better capture the motion information of objects, some methods now employ 4D Doppler point clouds collected by frequency-modulated continuous-wave (FMCW) LiDAR to enhance the detection and tracking of moving objects. Compared to standard time-of-flight (ToF) LiDAR, FMCW LiDAR can provide the relative radial velocity of each point through the Doppler effect, offering a more detailed understanding of an object’s motion state. However, despite the proven efficacy of these methods, ablation studies reveal that the direct contribution of Doppler velocity to tracking is limited, with performance gains often resulting from improved object recognition and labeling accuracy.Revisiting the role of Doppler velocity, this study proposes DopplerTrack, a simple yet effective learning-free tracking method tailored for FMCW LiDAR. DopplerTrack harnesses Doppler velocity for efficient point cloud preprocessing and object detection with O(N) complexity. Furthermore, by exploring the potential motion directions of objects, it reconstructs the full velocity vector, enabling more direct and precise motion prediction. Extensive experiments on four datasets demonstrate that DopplerTrack outperforms existing learning-free and learning-based methods, achieving state-of-the-art tracking performance with strong generalization across diverse scenarios. Moreover, DopplerTrack runs efficiently at 120 Hz on a mobile CPU, making it highly practical for real-world deployment. The code and datasets have been released at https://github.com/12w2/DopplerTrack.