Object state estimation always suffers low accuracy in complex traffic flow scenario due to noise interference and vehicle driving state changing. To solve these problems, a Kalman filter-based multi-object full lifecycle state estimation method is proposed for millimeter-wave radar, which includes both parameter initialization and online updating. Firstly, the Kalman filtering-based model is designed for multi-object full lifecycle state estimation in complex traffic flow scenario. Then, a data-driven approach is innovatively proposed for the observation noise covariance matrix initialization in Kalman filter. Furtherly, a variational Bayesian method is applied to update the Kalman filter parameters online for further enhancing the accuracy of multi-object full lifecycle state estimation. Finally, experimental data collecting from real vehicles are utilized to analyze the proposed method. The results show that the mean square error of this method is 0. 153 in multi-object state estimation, which is reduced by 36. 2% when compared with that of traditional Kalman filter. The comparison results evaluate the effectiveness of the proposed method on vehicle perception.