Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments

Abstract

This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle’s velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making.

ROTracker: a novel MMW radar-based object tracking method for unmanned surface vehicle in offshore environments

“…There are two baseline methods for the quantitative evaluation. The first baseline method is a traditional MMW radar-based tracking method SRT (Tan et al, 2023) which adopts a Kalman tracker. As the LiDAR-based tracking method LSTM (Yao et al, 2023) shows great performance, we also choose it as the baseline method.…”

Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar

“…Clustering, often employed as an unsupervised learning technique, is a cornerstone for point cloud data processing in object detection. In the current research, researchers focused on improving the performance of density-based spatial clustering of applications with the noise (DBSCAN) algorithm and combined it with other data aggregation algorithms to enhance object detection accuracy and speed [23,24]. Typically, Wang [25] incorporated DBSCAN into merged data and introduced frame order features to mitigate multipath noise and distinguish target points from noise.…”

“…Xie [26] utilized a multi-frame merging strategy to bolster single-frame clustering accuracy while leveraging frame sequence attributes to address multi-target noise. However, traditional MMW radar has several limitations, including its incapacity to capture height information [24], inability to detect stationary objects, and limited resolution. Therefore, these constraints render it ill-suited for autonomous driving and traffic perception.…”