Multiobject　Tracking　(MOT)　is　one　of　the　most　important　abilities　of　autonomous　driving　systems.　However,　most　of　the　existing　MOT　methods　only　use　a　single　sensor,　such　as　a　camera,　which　has　the　problem　of　insufficient　reliability.　In　this　paper,　we　propose　a　novel　Multiobject　Tracking　method　by　fusing　deep　appearance　features　and　motion　information　of　objects.　In　this　method,　the　locations　of　objects　are　first　determined　based　on　a　2D　object　detector　and　a　3D　object　detector.　We　use　the　Nonmaximum　Suppression　(NMS)　algorithm　to　combine　the　detection　results　of　the　two　detectors　to　ensure　the　detection　accuracy　in　complex　scenes.　After　that,　we　use　Convolutional　Neural　Network　(CNN)　to　learn　the　deep　appearance　features　of　objects　and　employ　Kalman　Filter　to　obtain　the　motion　information　of　objects.　Finally,　the　MOT　task　is　achieved　by　associating　the　motion　information　and　deep　appearance　features.　A　successful　match　indicates　that　the　object　was　tracked　successfully.　A　set　of　experiments　on　the　KITTI　Tracking　Benchmark　shows　that　the　proposed　MOT　method　can　effectively　perform　the　MOT　task.　The　Multiobject　Tracking　Accuracy　(MOTA)　is　up　to　76.40%　and　the　Multiobject　Tracking　Precision　(MOTP)　is　up　to　83.50%.