Abstract: Vehicle positioning and navigation is the basis of realizing environment perception of intelligent vehicles. To solve the error problem of intelligent vehicles under SINS/GPS integrated navigation, this paper proposes a method of improving vehicle positioning and navigation accuracy based on an improved support vector machine with ant colony algorithm. Firstly, an extended Kalman filter with state transformation is proposed to reduce noise of the integrated navigation system. Secondly, the support vector machine and the neural network aided navigation are proposed to solve the problem of large position error and the influence on navigation effect in the integrated navigation. Then, the support vector machine is improved by ant colony algorithm, and the kernel function parameters of the support vector machine are optimized iteratively. Finally, it is compared with the neural network assistance in the real vehicle collection data set. The results show that the neural network can reduce the root mean square value of error by 72.88%, 68.66% and 63.87% in the three directions of east, north and up (ENU), while the improved support vector machine can achieve 82.09%, 79.62% and 90.14%. The improved support vector machine can help optimize the position error of the integrated navigation and improve the accuracy of vehicle positioning and navigation.