A Generative Model Approach for LiDAR-Based Classification and Ego Vehicle Localization Using Dynamic Bayesian Networks  
[PERSON]  
1 Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (OTIEN), University of Genova, Via Opera Pa 11a, I-16145 Genoa, Italy; [EMAIL_ADDRESS] (M.A. ); [EMAIL_ADDRESS] (P.Z. ); [EMAIL_ADDRESS] (L.M. ); [EMAIL_ADDRESS] (C.R.) [PERSON]  
1 Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (OTIEN), University of Genova, Via Opera Pa 11a, I-16145 Genoa, Italy; [EMAIL_ADDRESS] (M.A. ); [EMAIL_ADDRESS] (P.Z. ); [EMAIL_ADDRESS] (L.M. ); [EMAIL_ADDRESS] (C.R.) [PERSON] Gomez  
sensors (e.g., Inertial Measurement Units (IMUs), steering angle sensors, wheel speed sensors), enabling AVs to perceive both external obstacles and their own motion [4; 5]. The ability to interpret and fuse data from multiple sensor sources [6; 7] forms the basis of a self-aware AV, allowing it to understand its surroundings, detect anomalies, and adjust its motion accordingly. AVs require precise and accurate perception of their surroundings to navigate safely in dynamic environments. Therefore, advanced sensors and data processing techniques are necessary to accomplish this goal [8; 9]. It is also possible to significantly enhance driving performance and safety by adding additional sensors [10; 11; 12] that monitor physiological signals. A significant advantage of LiDAR sensors is their ability to map the environment in 3D and provide greater accuracy in distance detection and measurement compared to traditional cameras [13; 14]. Nevertheless, preprocessing the dense 3D point clouds generated by LiDAR is essential for removing noise and obtaining relevant information [15]. An object's position, velocity, count, and classification can be determined using LiDAR sensors, which provide a 3D model of the surrounding environment. Multi-Target Tracking (MTT) for AVs involves the detection and tracking of multiple objects at once. Traditionally, detection-based tracking has been used in computer vision. However, it has now been adapted for use with LiDAR data [16]. As part of MTT, LiDAR data are segmented into meaningful clusters and tracked over successive frames in order to provide AVs with an understanding of their surroundings [17]. The use of sensor fusion techniques, which integrate LiDAR and radar data, has been investigated in a variety of research areas in order to improve target perception and detection performance in AV systems [18]. The use of deep learning frameworks for classifying landmarks has been proposed as a means of optimising vehicle localization [19]. In addition, LiDAR and camera data have been integrated to improve environmental perception [20]. Multiple object detection and tracking (MODT) algorithms, which employ multiple 3D LiDARs, are used to enhance tracking accuracy by utilizing grid-based clustering techniques and advanced filtering techniques [21]. [PERSON] et al. [22] proposed a method for learning 3D LiDAR data based on temporal association representations. It is, however, often overlooked in these studies that the interaction between dynamic and static objects needs to be modeled. With our approach, we provide a framework that not only categorizes static and dynamic tracks, but also examines how ego vehicle clusters interact with track clusters. AVs require, indeed, interaction modeling to gain self-awareness [23] and improve their navigation and decision-making capabilities. A key aspect of AV perception is accurately perform classification and localization of objects, which are essential for safe and efficient navigation. Classification allows AVs to distinguish between static (e.g., buildings, traffic signs, poles) and dynamic (e.g., vehicles, cyclists, pedestrians) objects. Accurate classification ensures that AVs can reliably interpret which objects remain fixed and which are moving, allowing them to anticipate interactions, make informed decisions, and optimize trajectory planning.