A Vehicle Target Tracking Method for Roadside Millimeter-Wave Radar
DOI:
https://doi.org/10.4108/eetsis.13388Keywords:
millimeter-wave radar, point cloud clustering, DZM-DBSCAN, target tracking, Kalman filtering, hungarian algorithmAbstract
INTRODUCTION: Roadside perception systems in intelligent transportation have stringent requirements for all-weather, high-precision, and real-time vehicle target tracking. However, there are inherent technical bottlenecks in such systems, including sparse millimeter-wave radar point clouds, severe noise interference, and the poor adaptability of traditional algorithms to distance-dependent density variations. OBJECTIVES: This study aims to propose a robust vehicle target tracking method based on roadside millimeter-wave radar to address the aforementioned technical bottlenecks and meet the stringent requirements of roadside perception systems for vehicle target tracking. METHODS: The research employs three key technical methods: Firstly, a point cloud distribution histogram is constructed to accurately localize the road area, and radar cross-section (RCS) characteristics are synergistically integrated with motion attributes to eliminate static clutter, multipath false plots, and electromagnetic noise. Secondly, a Distance-Zoned Multi-Frame DBSCAN (DZM-DBSCAN) clustering algorithm is proposed, which partitions the radar’s effective detection range into near-distance and far-distance intervals based on the physical principle of point cloud density attenuation with increasing detection distance, dynamically optimizes clustering parameters (Eps, MinPts) for each interval using local density statistics from k-nearest neighbor (KNN) analysis, and suppresses single-frame isolated false clusters through inter-frame motion continuity constraints. Finally, a multi-target tracking framework is established by combining a dynamically threshold-adjusted Kalman Filter (KF) with multi-feature weighted Hungarian matching. RESULTS: The main results obtained in this paper are the following: Experimental validation on the public RadarData dataset shows that the DZM-DBSCAN algorithm achieves a Silhouette Coefficient (SC) of 0.8456, representing a 10.09% improvement over the traditional DBSCAN (0.7681), and a Davies-Bouldin Index (DBI) of 0.1936, which is 10.66% lower than that of the traditional DBSCAN (0.2167). This effectively resolves the long-distance target missing detection issue prevalent in conventional methods. Additionally, the proposed tracking method achieves an optimal balance between tracking accuracy and continuity, with an average single-frame processing time of only 1.08 ms, fully meeting the real-time requirements of roadside perception systems. CONCLUSION: The proposed robust vehicle target tracking method based on roadside millimeter-wave radar provides a reliable all-weather, high-precision, and real-time roadside vehicle perception solution for intelligent transportation systems. Thus, this work holds significant engineering application potential and theoretical value.
References
[1] Yi X, Rui Y, Ran B, et al. Vehicle‒Infrastructure Cooperative Sensing: Progress and Prospect[J]. Strategic Study of Chinese Academy of Engineering, 2024, 26(1): 178-189.
[2] Liu L, Lu S, Zhong R, et al. Computing systems for autonomous driving: State of the art and challenges[J]. IEEE Internet of Things Journal, 2020, 8(8): 6469-6486.
[3] Zhang X, Wang H, Dong H. A Survey of Deep Learning-Driven 3D Object Detection: Sensor Modalities, Technical Architectures, and Applications[J]. Sensors, 2025, 25(12): 3668.
[4] Wang S, Mei L, Liu R, et al. Multi-modal fusion sensing: A comprehensive review of millimeter-wave radar and its integration with other modalities[J]. IEEE Communications Surveys & Tutorials, 2024, 27(1): 322-352.
[5] Yan B, Roberts I P. Advancements in Millimeter-Wave Radar Technologies for Automotive Systems: A Signal Processing Perspective[J]. Electronics, 2025, 14(7): 1436.
[6] LIU Zhihao, SHANG Yuanyuan, LI Timing, et al. Robust multi-drone multi-target tracking to resolve target occlusion: A benchmark[J]. IEEE Transactions on Multimedia, 2023, 25: 1462-1476.
[7] KULKARNI O, BURHANPURWALA A. A survey of advancements in DBSCAN clustering algorithms for big data[C]//2024 3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and Its Control (PARC). Mathura, India. IEEE, 2024: 106-111.
[8] SINGH H V, GIRDHAR A, DAHIYA S. A literature survey based on DBSCAN algorithms[C]//2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS). Madurai, India. IEEE, 2022: 751-758.
[9] Ester M, Kriegel H P, Sander J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland, USA: AAAI Press, 1996: 226-231.
[10] Sander J, Ester M, Kriegel H P, et al. Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 169-194.
[11] Zhu L, Liu G, Hui G, et al. An Unmanned Vehicle Inspection System for Airport Runways[C]//2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2022: 1171-1176.
[12] MIN Xiangqiang, HUANG Yi, SHENG Yehua. Automatic determination of clustering centers for “clustering by fast search and find of density peaks”[J]. Mathematical Problems in Engineering, 2020, 2020: 4724150.
[13] Zhang X, Zhou S. WOA-DBSCAN: application of whale optimization algorithm in DBSCAN parameter adaption[J]. IEEE Access, 2023, 11: 91861-91878.
[14] Jin X, Yang H, He X, et al. Robust LiDAR-based vehicle detection for on-road autonomous driving[J]. Remote Sensing, 2023, 15(12): 3160.
[15] El Yabroudi M, Awedat K, Chabaan R C, et al. Adaptive DBSCAN LiDAR point cloud clustering for autonomous driving applications[C]//2022 IEEE International Conference on Electro Information Technology (eIT). IEEE, 2022: 221-224.
[16] Mei F, Zhou H, Su J, et al. A Kalman filter‐Hungarian algorithm with a postprocessor for tracking aeolian saltating particle in high‐speed video[J]. Earth Surface Processes and Landforms, 2024, 49(15): 5086-5097.
[17] Sengupta A, Cheng L, Cao S. Robust multiobject tracking using mmwave radar-camera sensor fusion[J]. IEEE Sensors Letters, 2022, 6(10): 1-4.
[18] Szűcs G, Borsodi R, Papp D. Multi-camera trajectory matching based on hierarchical clustering and constraints[J]. Multimedia Tools and Applications, 2024, 83(15): 44879-44902.
[19] Chang S Y, Wu H C, Kao Y C. Tensor extended Kalman filter and its application to traffic prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(12): 13813-13829.
[20] Nai K, Li Z, Wang H. Dynamic feature fusion with spatial-temporal context for robust object tracking[J]. Pattern Recognition, 2022, 130: 108775.
[21] Yuan X, Wang S, Xie Y, et al. Object-Based Semantic Fusion Algorithm of Lidar and Camera via Inverse Projection[J]. IEEE Transactions on Instrumentation and Measurement, 2025.
[22] RadarData. Calibration and target tracking of roadside millimeter-wave radar [EB/OL]. Guangzhou: Pazhou Laboratory (Whampoa), China, 2023[2025-09-01]. https://iacc.pazhoulabhuangpu.com/contestdetail?-id=64af500-a4a0ed647faca6230&-award=1,000,000. unpublished
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