## 1 Introduction  
Localization is vital for connected and automated vehicles (CAVs) [1, 2]. Satellite-based localization, reliant on global navigation satellite system (GNSSs), encounters difficulties in satellite-denied areas like underground parking scenes [3]. The literature categorizes non-GNSS vehicle localization into wireless-based [4, 5, 6], LiDAR-based [7, 8, 9], and vision-based methods [10, 11]. However, wireless-based methods suffer from unstable signal transmission and the costly deployment of access points. LiDAR-based localization methods will also face challenges in data storage, transmission, and updates for a long time to come. Additionally, the high cost of LiDAR makes it less suitable for large-scale industrial applications.  
With the rapid development and cost reduction of onboard camera manufacturing technology, vision-based localization methods have been widely utilized in CAVs [12, 13]. Classic vision-based localization methods include visual odometry (VO) [14, 15, 16], visual map-based localization [17; 18; 19], and visual simultaneous localization and mapping (V-SLAM) [20; 21; 22]. However, in practice, VO and V-SLAM methods suffer from cumulative error issues in long-term localization [23; 24; 25]. High-definition (HD) visual maps can provide accurate and stable positional references, which can minimize the accumulation of localization errors. Nevertheless, HD maps store a large amount of static high-precision information and other dynamic information, resulting in significant data volume and storage space requirements, as well as matching delays and low matching efficiency. Lightweight maps take up less storage space than high-precision maps [26], but because of the small magnitude of the features that they store, the accuracy of the localization based on lightweight maps is relatively low. Trajectory predictions can predict the possible future position of a vehicle by analyzing the vehicle's historical motion trajectory, speed, acceleration, and other dynamic information. This prediction can be combined with real-time visual odometry to correct and optimize the localization results, thereby improving the localization accuracy [27; 28]. In complex environments or areas with large signal interference, trajectory prediction can effectively compensate for localization errors.  
Based on the above considerations, we propose a sequential processing strategy integrating enhanced berth mapping with Clothoid trajectory prediction to address the above-mentioned challenges and extend the localization services to underground parking scenes. The fundamental process involved generating a lightweight map containing multidimensional information based on the key berths in underground parking scenes. Furthermore, it triggered a localization mechanism that matched the trajectory prediction result.  
The remaining parts of this paper are organized as follows. Section 2 presents some related work. Section 3 describes the proposed localization method in the GNSS-denied underground parking scenes. The experiments were conducted on real-world datasets and are discussed in Section 4. Finally, Section 5 provides the conclusion of the proposed intelligent underground localization method.