## 1 Introduction  
Currently, many countries are vigorously developing intelligent, connected, vehicle-road cooperative technologies [1]. Intelligent connected vehicles have become a strategic direction for the development of the global automotive industry [2]. The high-precision dynamic positioning of highway vehicles serves as the foundation and a prerequisite for achieving intelligent connected transportation [3]. A high-precision spatio-temporal reference service is listed as the key basic technology that needs to be established [4], and the high-precision intelligent navigation of unmanned vehicles under the condition that satellites are not available is listed as one of the ten major engineering and technical problems [5].  
There are two main methods for vehicle mobile positioning: satellite navigation and inertial navigation [6]. As long as a Global Navigation Satellite System (GNSS) can receive the navigation satellite signal, it can calculate the position and speed of the vehicle [7]. An Inertial Navigation System (INS) works based on the inertial principle and does not need any external information when working, but navigation errors accumulate over time [8].  
In general road environments, a GNSS can be applied to achieve vehicle positioning [9]. In short-range shielded environments, vehicles can maintain a good continuous positioning performance based on a combination of a GNSS and INS [10; 11; 12]. However, in strongly shielded environments, such as long tunnels, effectively suppressing the divergence speed of the INS is an important issue that urgently needs to be addressed [13; 14]. When GNSS signals are unavailable because of obstruction, multipath effects, etc., the GNSS/INS/odometer (OD) combination can effectively mitigate the accumulation of INS errors to a certain extent, achieving the high-precision stable navigation and positioning of the vehicle [15; 16].  
To simulate the positioning performance of tunnel vehicles using a GNSS/INS/OD combination, the authors conducted experiments on a certain section of the highway where all GNSS signals were artificially removed. Only inertial navigation and the odometer were used for dead reckoning, and the GNSS/INS data fusion results were used as the reference trajectory (truth value) for comparison. The experimental results indicate that as the dead reckoning distance increases, the positioning results deviate more and more from the true trajectory. At a distance of 545 m, the positioning error is approximately 0.88 m [17]. This cannot meet the practical requirements for tunnel vehicle positioning.  
The reason for this lies in the fact that, during vehicle motion, the output of inertial devices contains significant noise. How to distinguish between valid data and noise and reduce the impact of vibration noise on system stability and positioning accuracy remains to be studied [18; 19; 20]. In different scenarios, when the effectiveness of sensors in the integrated navigation system changes, corresponding adjustments need to be made to the state space model [21; 22]. Multi-sensor systems are often heterogeneous due to their different principles, making it a challenge to construct reliable multi-sensor fusion function models that can accommodate the real-time addition and removal of sensor types [23; 24].  
Based on this, this paper uses high-precision digital lane maps, as well as the advantages of LiDAR in distance measurement and odometry in mileage counting, to propose a combined GNSS/INS/OD positioning method for tunnel vehicles based on lateral distance measurement and lane line constraints. It mainly consists of two steps: (1) the lateral distance measurement of vehicles inside a tunnel based on laser radar point cloud data; (2) map matching combined positioning based on lateral distance measurements, odometer, and lane markings. The prerequisite for this positioning method is that a high-precision electronic map of the tunnel has previously been measured or obtained.