3D multi-object tracking (3D MOT) is a key area in the field of autonomous driving. In systems that track by detection, the detection results of deep learning models will inevitably have FP(False Positives) and FN(False Nagatives), and detector always cannot continuously and accurately detect targets when facing obstacle occlusion and sensor blind spots. The task of 3D-MOT is to combine the discrete and disordered target detection results in time sequence into continuous and reliable tracks for use by downstream planning modules. At present, multi-target tracking algorithms in the field of autonomous driving are all based on single-hypothesis. In crowded scenarios, both false negatives (FN) and false positives (FP) significantly increase, making it difficult for single-hypothesis-based tracking algorithms to accurately output tracks. Towards this end, we propose LMH-MOT, a light multiple hypothesis framework for 3D MOT. Specifically, LMH-MOT effectively handles complex data association problems in autonomous driving scenarios by generating and maintaining multiple sets of hypotheses. Recognizing the possibility of switching between different motion states of the object, we use multiple motion models to more accurately estimate the motion state of the same object at the same time, and select the best estimation result for output. Additionally, we introduce a data association method based on decision trees, making full use of various features of the track and greatly reducing false matches and missing matches. In order to ensure the real-time performance of the entire algorithm framework, we also use gibbs sampling to significantly reduce the calculation time. On the NuScenes dataset, our proposed method achieves state-of-the-art performance with 76.2% AMOTA.