We propose LIO-Vehicle, a new tightly-coupled vehicle dynamics extension of LiDAR inertial odometry (LIO) method that provides highly accurate, robust, and real-time vehicle trajectory estimation. Since most existing LiDAR-based localization methods are not specifically proposed for vehicles, they do not take the motion constraints of ground robots into account. And they may not work well in structure-less areas, such as tunnels and narrow corridors. For LIO in these LiDAR-degraded circumstances, inertial sensors alone are unable to sustain reliable long-term accuracy due to the accumulation of errors without external periodic corrections. Therefore, it is necessary to introduce other low-cost sensors and vehicle motion constraints to build a more accurate and robust odometry algorithm. In this letter, we use wheel speedometer and steering angle sensor measurements to establish a two-degree-of-freedom vehicle dynamics model and then construct a preintegration factor based on the model’s output. At the backend, we add the vehicle dynamics preintegration results, IMU preintegration measurements, and LiDAR odometry results to a factor graph and get the optimized result with the help of sliding window optimization. The experiments show that the proposed method can achieve higher positioning accuracy compared with the existing LiDAR inertial odometry methods and it can significantly mitigate navigation error in harsh areas where environmental features are insufficient for LiDAR odometry.