Lane-crossing detection of vehicles is an important part of intelligent transportation system. To tackle this issue, we proposed a lane-crossing detection method of vehicles with in-vehicle image. First, we use synthesizing-data method to build a rich and varied lane-crossing detection dataset. Then, we use image semantic segmentation to detect vehicle and lane lines, and then we estimate wheels positions of vehicle. Finally, we contrast wheels positions with lane lines positions to judge whether there is lane-crossing behavior. Experiment results show that combined with semantic segmentation model, we achieve an average precision of 88.7% for lane-crossing detection, and the average detection time is 35 ms, which means that the proposed method has certain practical application value.