Tracking vehicle trajectories is essential for autonomous vehicles and advanced driver-assistance systems to understand traffic environment and evaluate collision risk. In order to reduce the position deviation and fluctuation of tracking on-road vehicle by millimeter-wave radar (MMWR), an interactive multi-model Kalman filter (IMM-KF) tracking algorithm including data association and track management is proposed. In general, it is difficult to model the target vehicle accurately due to lack of vehicle kinematics parameters, like wheel base, uncertainty of driving behavior and limitation of sensor’s field of view. To handle the uncertainty problem, an interacting multiple model (IMM) approach using Kalman filters is employed to estimate multitarget’s states. Then the compensation of radar ego motion is achieved, since the original measurement is under the radar polar coordinate system. Taking into account the real-time performance of the algorithm and the distinguishability of vehicles involved in traffic, the nearest neighbor data association (NNDA) is used to associate observation with trajectory, which is fast and stable. And after the process of track establishment, confirmation, continuous updating, supplement and extinction, the multi-track management of vehicle targets is realized. Finally, two normal traffic scenarios including lane changing on straight freeway and turning on intersection are designed to test the feasibility and validate the tracking algorithm on PreScan simulation platform. Compared with the original radar data and UKF filtering results, the tracking algorithm in this paper achieves good stability and accuracy.