Online object detection and tracking are crucial for embodied intelligence systems, including autonomous vehicles and robotics. Traditional approaches employ a pipeline structure to perform detection and tracking separately, which can not fully leverage information from the detector. Moreover, most prior tracking methods rely on motion models such as constant velocity for state updates, which can lead to incorrect associations when the velocity estimates are inaccurate. To address these limitations, we propose ConTrack3D, an online tracking approach that jointly performs detection and tracking in an end-to-end manner. Specifically, ConTrack3D incorporates a Joint Encoder module to capture detection embeddings and a Temporal Extender module for data-driven state updates. By employing contrastive learning, ConTrack3D learns discriminative tracking representation for more accurate association. ConTrack3D is evaluated on the nuScenes benchmark, and the experimental results demonstrate its significant improvements in tracking performance.