Multiple object tracking (MOT) is becoming increasingly significant for autonomous driving and intelligent transportation systems. However, traditional MOT methods cannot track the objects accurately and robustly due to the lack of effective feature extraction and data association in complex traffic scenarios. In this paper, we propose a novel joint detection and tracking method AttentionTrack by introducing multiple features attention. Firstly, we design a self-motivated feature extraction attention network (FEAN) to adaptively produce effective decoupled features for detection and tracking tasks in different scenarios. Secondly, we build a spatial-temporal data association (STDA) framework to achieve more accurate and robust tracking by considering the historical features of trajectory through different times. Moreover, we conduct comprehensive experiments on the KITTI, UA-DETRAC and MOT17 benchmarks, and the results show that our approach achieves competitive performance compared with the state-of-the-art (SOTA) trackers.