## 2 Literature Review
Then, the pose relationship between the current vehicle and the map is calculated using the matched feature points to complete the localization. Sparse feature-based matching methods are commonly used in this process [34; 35], such as scale-invariant feature transform (SIFT) [36], speeded-up robust features (SURF) [37], oriented FAST and rotated BRIEF (ORB) [38], and others [13; 39; 40]. Although sparse feature-based map-matching methods satisfy the requirement of minimizing time complexity, they suffer from insufficient matching accuracy. To improve the matching accuracy, research has proposed localization methods such as hierarchical matching using the bag-of-words (BoW) model [41; 42] and multi-view localization [31]. These methods aim to enhance the success rate of map matching by switching perspectives or levels. Another approach to address map-matching failures is to adopt a \"coarse-to-fine\" matching strategy. For example, based on seqSLAM sequence matching, a multi-resolution localization method has been proposed using particle filter algorithms [43]. However, this process of moving from coarse node-level localization to more precise metric-level localization involves accumulated errors, and the localization accuracy is highly dependent on node density. In recent years, trajectory prediction has been considered in the localization problem to improve the localization accuracy [27; 28]. The core of the trajectory prediction is to deeply analyze and learn the historical motion trajectory of the moving object to predict its future motion trend and position. In the localization problem, trajectory prediction can provide the localization algorithm with prior information on the future position [44]. This prior information can help the algorithm to maintain a high positioning accuracy even when the signal quality is poor. At the same time, trajectory prediction can help reduce the noise and outliers in the sensor data, thereby improving the positioning accuracy. Underground parking scenes are indoor environments with dense architectural obstructions, limiting the satellite signals. In such cases, low-cost visual cameras can be used for localization. One method is to utilize surveillance cameras in the parking lot to detect vacant parking spaces, thereby assisting in localization [45]. However, this localization method can only roughly estimate the positions of the vacant parking spaces. It cannot obtain precise information about berth and vehicle poses, requiring manual parking and having limited automation. Another method is to extract the berth codes from the around-view images captured by onboard cameras for matching, and further introduce visual fiducial markers to improve the overall localization accuracy [46]. However, the visual fiducial markers, similar to quick response (QR) codes, are prone to occlusion, rendering them ineffective. Specifically, when the reference object is occluded, there may be a slight overlap between the current image and the visual map's field of view, resulting in map-matching failures and reduced localization accuracy. In summary, existing vehicle localization methods for underground parking scenes face two unresolved issues: (1) a large data volume, matching latency, and low efficiency in HD maps; (2) a low matching success rate between the target localization node and the nearest map node, resulting in a low localization accuracy. To address these problems, an enhanced berth mapping model is proposed, which generates a lightweight map that improves the map retrieval efficiency and significantly reduces the map storage. Furthermore, trajectory prediction, especially the trajectory prediction method based on the Clothoid theory [47; 48; 49], has a high trajectory calculation accuracy and can provide high-precision running trajectories of the vehicle to assist in improving the localization accuracy. Therefore, a multimodal localization method with the lightweight map and the Clothoid trajectory prediction is proposed.