## 2 Related Works
### Ego Vehicle Localization  
AVs require accurate localization, especially in environments with limited GPS reliability. As a result of recent developments utilizing LiDAR technology, localization capabilities have been significantly enhanced: LiDAR SLAM based multi-vehicle cooperative localization using iterated split Covariance Intersection Filters (Split CIFs) is presented in [41]. Using Split CIFs with decentralized SLAM, this study enhances localization accuracy and robustness across AV networks. Based on tests performed using the CARLA simulator, their methodology demonstrates substantial improvements over traditional SLAM techniques. Persistent Homology (PH) is applied to ego-vehicle localization in [42]. PH is a topological data analysis method that is designed to extract translation and rotation invariant features from point clouds, thereby capturing their global structure without taking into consideration their local details. As a result of vectorizing these features into Persistence Images (PI), the authors are able to create fixed-size vectors from variable-size point clouds. There are three applications investigated in this paper: loop closure detection, which identifies previously visited locations based on PIs; place categorization, which effectively differentiates distinct shapes based on the global structure of point clouds; and end-to-end global localization, which integrates PIs into neural networks to improve localization accuracy. Although PIs enhance position estimation to a certain extent, they do not significantly improve orientation accuracy due to the loss of local geometric information. The study concludes that, while PH is useful for extracting global features, it is not able to capture the local geometry necessary for precise localization.  
In [24], a novel method is developed to enhance vehicle localization precision in urban canyons obstructed by GPS signals by combining LiDAR with real-time kinematic positioning. With this method, the localization error can be reduced to within a few centimeters, even in densely populated areas. An innovative SLAM algorithm introduced in [43] integrates LiDAR data with inertial measurements to improve mapping accuracy in urban environments. By using this method, drift is reduced in densely populated urban areas where satellite signals are frequently obstructed. A sensor fusion technique is proposed in [44] that integrates radar and LiDAR data. It can maintain high localization accuracy even in adverse weather conditions, addressing some of the limitations of LiDAR systems, such as fog and heavy rain.  
In [45], a method of precise ego-location is developed by integrating multi-view imaging with LiDAR and vectorized maps. A transformer decoder is applied in this approach, which is validated across diverse urban settings and significantly improves pose estimation over traditional approaches. Our method for ego vehicle localization differs from the aforementioned methods, as it employs a generative model based on DBNs and interaction dictionaries, leveraging pre-classified static tracks learned during training. We use an MJPF for testing, which combines continuous tracking using Kalman filters with non-linear estimation using particle filters. This approach refines the ego vehicle's position by comparing predictions with real-time observations, allowing us to detect errors and anomalies effectively.