On the other hand, localization refers to the AV's ability to accurately determine its position and motion within an environment. Traditional localization methods, such as GPS and odometry-based approaches, often suffer from signal loss, drift, and inaccuracies in complex environments, e.g., urban canyons, tunnels, and cluttered environments. Therefore, alternative localization techniques are required to enhance reliability and robustness, particularly in dynamic environments where environmental conditions constantly change. In this work, LiDAR-based perception is utilized to enhance both classification and localization capabilities. LiDAR generates high-resolution 3D point clouds, which allow AVs to precisely map their surroundings and distinguish between different object categories [8; 9]. LiDAR's ability to capture spatially rich depth information makes it superior to vision-based systems, particularly in adverse lighting conditions, poor weather, and complex urban environments [24]. The classification of LiDAR-based tracks into static and dynamic categories provides landmark references for localization, ensuring that AVs can estimate their position accurately even in the absence of GPS signals or external odometry updates [25]. By leveraging LiDAR-based classification techniques, AVs can use static objects as anchor points for precise localization, while simultaneously tracking and interpreting the motion of dynamic objects. The motivation for this research extends beyond the intrinsic benefits of LiDAR sensors to its application in the pipeline used to classify and localize AVs. Navigating precisely in dynamic environments requires not only the perception of static and dynamic objects, but also their effective use for localization [26; 27; 28]. Using LiDAR's capability to generate 3D point clouds, we have proposed a classification method that identifies static and dynamic tracks in the ego vehicle environment [29]. Static tracks provide reliable landmarks for ego vehicle localization because they remain invariant over time. Dynamic tracks, on the other hand, pose difficulties because of their motion [30; 31]. However, the ability to understand interactions with dynamic tracks is essential. Based on this duality, we focused on accurately classifying tracks during the training phase using advanced algorithms such as Joint probababilistic data association (JPDA), Growing neural gas (GNG), and interaction dictionaries. The resulting classified static tracks are then used during testing to determine the ego vehicle's location without relying on external odometry updates, thereby ensuring robustness in real-world conditions. These concepts are explicitly integrated into a cognitive self-awareness cycle. An AV can acquire knowledge of the structure of its environment, generate dictionaries of potential interactions with a variety of static objects, and characterize the dynamics of moving objects during the offline training phase. Afterward, during the online testing phase, the AV's pose is continuously monitored, anomalies are detected, and the system is able to adapt to unanticipated circumstances by fusing the acquired knowledge with new sensor data using probabilistic filters, specifically an MJPF. In essence, classification and localization serve as fundamental components of the broader self-awareness strategy, continuously interacting with higher-level modules. Recent generative modeling techniques have shown strong potential in enhancing AV perception and trajectory predictions. For instance, diffusion-based models such as LiDAR [32] have demonstrated effective LiDAR-based human motion prediction in dynamic scenes. Similarly, [PERSON] et al. [33] introduced a Bayesian ensemble graph attention network to learn spatio-temporal relationships in dense traffic for multi-agent prediction. Generative transformer models [34] have also been employed to learn interpretable latent spaces for trajectory forecasting, particularly in multi-agent environments.