## 1 Introduction  
Smart cars and autonomous driving technology have made very significant advances in the last decade, and vehicle location technology has been a key technology in smart driving. Traditional vehicle positioning techniques [1; 2; 3; 4] include the use of GPS, inertial navigation, image recognition, radar, etc. However, in some complex scenarios, such as in densely built-up areas or long tunnels, GPS can lose lock [5], and lane markings can be obscured on congested roads at low speeds. These conditions can result in existing positioning calculation methods, such as GPS, inertial navigation, vision, and LIDAR not being able to accurately locate the vehicle. The use of a single positioning system or single sensor positioning technology for vehicles is no longer sufficient for the complex traffic conditions of today; therefore, more and more researchers are trying to use multi-sensor fusion localization methods for precise positioning of vehicles [6; 7; 8; 9]. Compared with a single positioning system. The multi-sensor fusion positioning method not only has higher accuracy but also has better robustness and anti-interference. [PERSON] et al., proposed interactive multi-model filtering (IMM-UKF) method to jointly estimate position information [10], fusing INS with GPS. Compared with the traditional Kalman filter, this algorithm has higher accuracy and convergence speed. Although this algorithm can provide higher accuracy of vehicle pose in a complex urban environment, such a method based solely on satellite signal enhancement and optimization cannot be used in a long tunnel and other GPS signals completely blind area. [PERSON] proposed a vehicle positioning system that relies only on heading projection and inertial navigation for vehicles that have been out of GPS signal lock for a long time [11]. The experimental results show that the system can effectively reduce the error to achieve effective positioning when the GPS is out of lock for a long time, but it only reduces the error for the longitudinal direction of the vehicle, the lateral positioning error of the vehicle is still large. [PERSON] et al., proposed a high-precision integrated navigation method for land vehicles using SINS, odometer, and Doppler radar [12]. The experimental results of the method show that the positioning accuracy of the vehicle in the absence of a GPS signal of the vehicle compared to only the inertial guidance system has significantly improved. Its horizontal position error although not more than 0.03% of the distance the vehicle travels, but due to the accumulation of its error, the positioning accuracy of the vehicle in the long tunnel will not meet the lateral positioning of the vehicle after traveling a certain distance. A loosely coupled algorithm for relative localization of the visual odometer was proposed by [PERSON] [13] et al., to update the unscented Kalman filter using the relative pose between two frames of the camera. Although it has a lower error than other visual odometer methods, the algorithm only addresses the longitudinal positioning of the vehicle and does not specifically investigate the lateral positioning of the vehicle. [PERSON] et al., used perspective transformation of images captured by a monocular camera for in-lane localization of vehicles [14]. Its positioning has high accuracy but only takes into account the distance of the vehicle from the left and right lane lines when the vehicle is traveling in a certain lane and can be considered a static positioning, which will not be applicable to the dynamic positioning of the vehicle when a lane change occurs. [PERSON] et al., proposed the method of integrating dead reckoning with inertial navigation to solve the problem of vehicle positioning when a GPS signal is lost [15]. Although the positioning accuracy is improved compared with the use of a single inertial navigation system, there is not much research on the lateral positioning of vehicles.