Abstract: Intelligent vehicles require high positioning accuracy and stability. With the development of the internet of vehicles and intelligent transport system (ITS) and V2X (Vehicle to Everything) technology, vehicle positioning can now be achieved using a wider range of data sources. Traditional positioning methods are highly dependent on GNSS, while existing V2X positioning methods ignore the effect of latency. This paper proposes a V2X-based cooperative positioning method (CPM) through an enhanced KF and a transmission delay compensation to achieve accurate positioning. In CPM, the roadside perception of the vehicle is transmitted to the OBU through the V2X platform and is processed to reduce the effect of latency by historical IMU data to reconstruct the position. Then, an enhanced KF-based data fusion method is introduced, which utilizes a parallel KF and a Kalman gain adjustment strategy to fuse the compensated roadside information with the IMU data to achieve an optimal estimation of the vehicle position. Real vehicle experiments demonstrate that CPM improves the positioning accuracy and reduces the error caused by time delay. Compared to roadside lidar positioning, the RMSE of CPM is reduced by 34.7% and 59.1% on two test tracks, respectively. In addition, an ablation experiment verified the effectiveness of each module in CPM, and a comparison experiment demonstrated the superiority of the proposed fusion method.