  The ability to perceive and comprehend a traffic situation and to estimate
the state of the vehicles and road-users in the surrounding of the ego-vehicle
is known as situational awareness. Situational awareness for a heavy-duty
autonomous vehicle is a critical part of the automation platform and depends on
the ego-vehicle's field-of-view. But when it comes to the urban scenario, the
field-of-view of the ego-vehicle is likely to be affected by occlusion and
blind spots caused by infrastructure, moving vehicles, and parked vehicles.
This paper proposes a framework to improve situational awareness using
set-membership estimation and Vehicle-to-Everything (V2X) communication. This
framework provides safety guarantees and can adapt to dynamically changing
scenarios, and is integrated into an existing complex autonomous platform. A
detailed description of the framework implementation and real-time results are
illustrated in this paper.
