When police carry out traffic law enforcement using multi-target speed measuring radar, one of the most essential things is to judge which lane each vehicle belongs to, and only in this way the captured pictures can serve as the law enforcement evidence. To achieve lane division purpose, traditional way is to obtain a fixed threshold by manual measurement and sometimes the method of coordinate system rotation is also needed, but this method has a large error with difficulty in operating. A new lane division algorithm called Kernel Clustering algorithm based on Statistical and Density Features (K-CSDF) was proposed, which includes two steps: firstly, a feature extraction method based on statistical feature and density feature was used to process the vehicle data captured by radar; secondly, a dynamic clustering algorithm based on kernel and similarity was introduced to cluster the processed data. Simulations with Gaussian Mixture Model (GMM) algorithm and Self-Organizing Maps (SOM) algorithm were conducted. Simulation results show that the proposed algorithm and SOM algorithm can achieve a lane accuracy of more than 90% when only 100 sample points are used, while GMM algorithm cannot detect the lane center line. In terms of running time, when 1000 sample points are taken, the proposed algorithm and GMM algorithm spend less than one second, and the real-time performance can be guaranteed, while SOM algorithm takes about 2.5 seconds. The robustness of the proposed algorithm is better than GMM algorithm and SOM algorithm when sample points have a non-uniform distribution. When different amounts of sample points are used for clustering, the proposed algorithm can achieve an average lane division accuracy of more than 95%.