Conventional density-based spatial clustering of applications with noise （DBSCAN） algorithms struggle in complex dynamic traffic due to non-uniform density of millimeter-wave radar point clouds， causing ambiguous boundaries， poor separability， and noise misclassification. This paper proposes a two-stage enhanced DBSCAN framework integrating adaptive polar grid partitioning with velocity-based post-processing. In stage one， a dynamic elliptical neighborhood search adapts its range according to radial distance， ensuring accurate initial spatial clustering under density variations. Stage two exploits Doppler radial velocity statistics： Outliers are removed via a velocity-consistency criterion， misclassified valid points are reassigned through a spatial-velocity dual-similarity framework， and overlapping clusters are split based on velocity dispersion and motion direction. Experimental results demonstrate significant improvements over traditional DBSCAN. In vehicle-overlapping scenarios， accuracy rises from 79.59% to 88.1%； in pedestrian-vehicle overlaps， from 86.55% to 97.24%. Evaluation metrics show the silhouette coefficient improves by 14.4%， the Calinski-Harabasz index by 381.3%， and the Davies-Bouldin index decreases by 41.5%， indicating clearer boundaries and better cluster stability. The proposed method effectively resolves overlaps and suppresses noise， offering a reliable preprocessing solution for millimeter-wave radar in dynamic environments.Highlights:1.Adaptive polar-grid DBSCAN with dynamic elliptical neighborhoods compensates for radial-distance-dependent density distortion, ensuring robust initial clustering under non-uniform point-cloud distribution.2.Velocity-consistency filtering and spatial-velocity dual-similarity reassignment accurately remove outliers and recover misclassified valid points, preserving target integrity.3.Overcome the inherent limitation of spatial-only DBSCAN by leveraging velocity dispersion and directional differences to split overlapping clusters, enabling accurate separation of adjacent targets with distinct motions.