旋转雷达多普勒速度测量能否改善车辆检测与跟踪？
Do Spinning Radar Doppler Velocity Measurements Improve Vehicle Detection and Tracking?
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中文总结 AI 辅助
本文研究旋转雷达多普勒速度测量对车辆检测与跟踪的改进作用，提出多普勒去畸变和速度先验方法，在Boreas数据集上分别提升检测精度2.37点（mAP）和跟踪精度13.68点（MOTA）。
中文摘要 AI 辅助
旋转调频连续波（FMCW）雷达因其对恶劣天气条件的鲁棒性和360°视场角，在自动驾驶车辆感知中日益受到青睐。近期，扫描雷达也被证明能够生成逐方位角的多普勒速度。本文探讨了这些多普勒速度测量是否能够提升旋转雷达的车辆检测与跟踪性能。在检测方面，我们估计自车运动，并利用其来消除雷达图像中的多普勒距离畸变，然后再将图像输入网络。在跟踪方面，我们提出了一种新的每车辆速度估计方法，并将其作为跟踪器运动模型的先验。由于目前没有任何带有动态目标真值标签的数据集包含支持多普勒的旋转雷达数据，我们的第一个贡献是构建了一个自动标注流程，该流程利用一组微调后的现成激光雷达检测器集成，对Boreas Road Trip数据集的全部643公里数据进行标注。随后，我们将检测结果迁移至雷达数据，并利用超过250公里的车辆密集序列作为真值训练数据。通过训练和评估两种最先进的检测器，我们证明多普勒去畸变可以将平均精度（mean average precision）的检测准确率提升高达2.37个百分点。此外，我们还证明，与零速度初始化基线相比，多普勒速度先验可以将多目标跟踪准确率（MOTA）提升13.68个百分点，同时达到使用真值速度作为先验所获MOTA的99.7%。
英文摘要
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° 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.
发表机构
- University of Toronto（多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。
