## 1 Introduction  
In recent years, the field of autonomous driving algorithms has seen rapid development. To achieve effective control in autonomous vehicles, it is essential to first obtain the vehicle's own attitude and position. To meet these positioning requirements, various methods such as GPS positioning, visual SLAM (simultaneous localization and mapping) positioning, UWB (ultra-wideband) positioning, and acoustic positioning have been continuously refined, each forming multiple positioning methods or integrated positioning algorithms. GPS positioning and visual SLAM positioning methods are relatively expensive and have specific operational constraints. GPS positioning requires an unobstructed line of sight to the sky above the vehicle's antenna to function properly, which limits its use in enclosed or urban environments. Visual SLAM, due to the processing speed of images, tends to have a lower frequency of positioning information, which can affect real-time performance. UWB positioning, while requiring the installation of base stations, is generally more cost-effective and provides high-frequency positioning information with better real-time capabilities. However, the extremely fast propagation speed of electromagnetic waves can result in lower positioning accuracy, which may not meet the demands of high-precision applications. In comparison, acoustic positioning also necessitates the setup of base stations, with costs similar to those of UWB positioning. The speed of sound is significantly lower than the speed of light, which allows for higher precision in signal transmission and reception. Therefore, acoustic positioning offers the advantages of low cost and high precision in small-scale scenarios, effectively meeting the positioning requirements. [PERSON] et al. [1] proposed an improved SRP-PHAT [2] method for vehicle horn sound localization. By analyzing the sound, frequency domain features for identifying vehicle horn sounds were selected, and a corresponding time-frequency transform (VHSRTFT)-based sound recognition method was proposed. [PERSON] et al. [3] proposed an algorithm for determining the distance to a sound source based on estimating the variation in incoming sound power and frequency with distance. Through this algorithm, objects can be located solely based on their acoustic characteristics without requiring any visual information. [PERSON] et al. [4] used the phenomenon of sound diffraction to embed specific directional features into the emitted sound signal. After one-time calibration in the deployment environment, the system learns the directional signal, thereby achieving the positioning function of the mobile robot. However, the positioning accuracy of the above methods is difficult to break through to a positioning accuracy of \(\pm 5\) cm. Therefore, this article aims to study how to stabilize the positioning accuracy within the range of \(\pm 5\) cm. This paper investigates the TOF method for ranging, which involves pre-estimating coordinates based on the distances from the base stations to the mobile terminal, thereby achieving positioning functionality. According to the principles of the TOF method, the measurement of time differences and the current speed of sound are key to enhancing ranging accuracy. In addition to this, after obtaining ranging information, it is necessary to perform coordinate calculations using an appropriate pre-estimation method. Focusing on the TOF ranging and positioning scenario with four base stations, this paper primarily explores how to reduce the errors in time difference measurements and how to select base stations and selectively choose ranging information for coordinate calculations. This article mainly explains the above issues in Sections 2 and 3. Section 2 describes the method of assigning high-precision confidence interval weights for acoustic ranging, while Section 3 discusses the deployment of base stations and self-optimization positioning methods, and it also presents experimental verification.