The safety of unprotected road-users is crucial in any traffic scenario. In most urban scenarios, there are multiple occlusions and blind spots, which can lead to unsafe situations. These occlusions are typically caused by buildings, moving vehicles, and parked vehicles. Situational awareness of an ego-vehicle is defined as the ability of the vehicle to perceive and comprehend traffic conditions while estimating the state of nearby vehicles and unprotected road users. Presently, this ability completely relies on the information provided by the sensors mounted onboard of the ego-vehicle. The information from onboard sensors is usually limited to their field-of-view. In this work, a specific scenario of an occluded pedestrian crossing is considered. The proposed approach is to leverage vehicle-to-everything~(V2X) communication to obtain information from sensors mounted on other vehicles or infrastructure, located in the near surrounding of the ego-vehicle. 

The objective is to build situational awareness by finding sensor information from both local and connected sensors. We focus on a framework that can handle varying uncertainties and provide safety guarantees for the road-users. The framework can compensate for measurement uncertainty and uncertainty in the initial state of the detected road-user. This framework employs an algorithm based on set-based estimation, where the noise is assumed to be unknown but bounded. This algorithm computes an estimated set by integrating measurement sets from both local and connected sensors for the unprotected road-user in the scenarios. To enhance computational efficiency, these sets are represented as convex polygons, specifically zonotopes and constrained zonotopes. We implement the framework in a real system, evaluating its feasibility, efficiency, and robustness under dynamic conditions. We conduct thorough testing, ensuring that the framework meets performance requirements and delivers reliable situational awareness in practical scenarios.