Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking?

Summary

Abstract Summary

Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustness to adverse weather conditions and 360{\deg} field of view. Recently, scanning radars have also been shown capable of generating per-azimuth Doppler velocity. In this paper, we investigate whether these Doppler velocity measurements improve spinning radar vehicle detection and tracking performance. For detection, we estimate the ego motion and use it to undo the Doppler range distortion of the radar image before passing it to a network. For tracking, we propose a new way to estimate a per-vehicle velocity and use it as a prior for the tracker's motion model. Since Doppler-enabled spinning radar data is not available in any dataset with ground-truth dynamic object labels, our first contribution is an automatic labelling pipeline that uses an ensemble of fine-tuned off-the-shelf lidar detectors to label all 643 km of the Boreas Road Trip dataset. We then transfer detections to radar, and use over 250 km of vehicle-dense sequences as ground-truth training data. By training and evaluating two state-of-the-art detectors, we show that Doppler undistortion can improve detection accuracy by up to $2.37$ points on mean average precision. Furthermore, we show that the Doppler velocity prior can improve tracking accuracy by $13.68$ points on multi-object tracking accuracy (MOTA) versus the zero-velocity initialization baseline, while achieving $99.7\%$ of the MOTA obtained using ground-truth velocities as the prior.

Key Points

Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustness to adverse weather conditions and 360{\deg} field of view.
Recently, scanning radars have also been shown capable of generating per-azimuth Doppler velocity
In this paper, we investigate whether these Doppler velocity measurements improve spinning radar vehicle detection and tracking performance.
For detection, we estimate the ego motion and use it to undo the Doppler range distortion of the radar image before passing it to a network.
For tracking, we propose a new way to estimate a per-vehicle velocity and use it as a prior for the tracker's motion model.

Methodology

Our labelling pipeline uses lidar observations to generate vehicle boxes, track identities, and velocities for spinning radar data. It runs offline on each sequence of Boreas-RT [ 18 ] , using its $10\text{\,}\mathrm{Hz}$, 128-beam Velodyne Alpha Prime lidar, $4\text{\,}\mathrm{Hz}$ Navtech RAS6 spinning radar, calibrated sensor extrinsics, and post-processed GNSS–INS poses.

Figure 2 follows the same vehicles through four stages: detect and fuse, track offline, fit trajectories, and transfer to radar.

Methodology — Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking? — Fig. 3: Skyway scene of Boreas-RT with a vehicle travelling at $32.1\text{\,}\mathrm{m}$, (a) raw and (b) after Doppler undistortion with the ground-truth ego velocity. Orange dashed boxes transfer the nearest lidar frame at its midpoint, and teal solid boxes sample the vehicle’s continuous trajectory at the estimated azimuth-crossing time. The two boxes are separated by $8.6\text{\,}\mathrm{m}$. The brackets mark the spread of the vehicle’s return. Note how the vehicle’s return in the undistorted scan fit tighter within the teal box, with the remaining offset due to the target’s own motion. Intensity is normalized per panel.

Experiments

We present the labels from our pipeline, then use them to evaluate Doppler undistortion for detection and the estimated prior for tracking. We summarize the generated labels here and assess them in the two parts that follow. The pipeline produced over $9.1$ million lidar boxes and over $3.7$ million radar boxes across 60 Boreas-RT sequences spanning $643\text{\,}\mathrm{km}$ of driving.

Table I reports the final outputs by route category. Figure 5 shows four MCTrack failure modes and how refinement resolves them.

Experiments — Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking? — Fig. 4: Single-scan Doppler velocity initialization for tracking, following one vehicle through three stages. (a–c) Doppler undistortion using the estimated ego velocity and an annular sector (purple) around a detection box (cyan). (d–g) Cross-correlation within the polar rectangle and consensus selection of per-azimuth radial velocities (red). (h–i) Projection onto the box heading, giving the velocity prior (teal), and track initialization. This Boreas-RT vehicle is located $12.8\text{\,}\mathrm{m}$ from the radar, with an estimated speed of $7.5\text{\,}\mathrm{m}$ against a ground-truth $8.8\text{\,}\mathrm{m}$.

Sources and Demonstrations

Figures are reproduced from Xie et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.