Multi-object tracking is a crucial area in autonomous driving. However, it is difficult to overcome the challenges of complex scenes by only relying on a single image or point cloud information, and the current multi-modal fusion tracking methods still have many problems in fusion strategy, data association and trajectory management. Therefore, we propose a 3D multi-object tracking network based on 3D volume information interaction between image and point cloud. Firstly, we design the 3D volume feature interaction module to obtain the 3D volume morphology information of the object, so as to get more discriminative features and improve the localisation accuracy in complex scenes. Secondly, we design the data association based on 3D integrated motion estimation, using Kalman filtering and the object's motion information in the point cloud to obtain the prediction of the object's position in the next frame, so as to enhance the consistency of the object between frames. Finally, to further enhance the robustness of trajectory association, we design a 3D volume feature-based trajectory management module to better overcome the object disappearance-reappearance association problem. Experimental results on the KITTI dataset show that the proposed method has better tracking performance compared with other methods.