Vehicle detection in traffic surveillance videos is a special subtask in object detection, where desired objects are vehicles moving on the road while the background is still within a sequence. The disparity of speed within each frame, i.e. moving and static, is consistent with the vehicle and background semantic to some extent, thus motions can be extracted to enhance the appearance of foreground. In this paper, we propose a motion prior embedded parallel architecture for vehicle detection, aiming at illuminating vehicles and suppressing false positives in the background. We further implement extensive experiments on the UA-DETRAC dataset to validate the effectiveness of our approach, and achieve promising performance in both accuracy and speed.