  One of the main challenges in developing autonomous transport systems based
on connected and automated vehicles is the comprehension and understanding of
the environment around each vehicle. In many situations, the understanding is
limited to the information gathered by the sensors mounted on the ego-vehicle,
and it might be severely affected by occlusion caused by other vehicles or
fixed obstacles along the road. Situational awareness is the ability to
perceive and comprehend a traffic situation and to predict the intent of
vehicles and road users in the surrounding of the ego-vehicle. The main
objective of this paper is to propose a framework for how to automatically
increase the situational awareness for an automatic bus in a realistic scenario
when a pedestrian behind a parked truck might decide to walk across the road.
Depending on the ego-vehicle's ability to fuse information from sensors in
other vehicles or in the infrastructure, shared situational awareness is
developed using a set-based estimation technique that provides robust
guarantees for the location of the pedestrian. A two-level information fusion
architecture is adopted, where sensor measurements are fused locally, and then
the corresponding estimates are shared between vehicles and units in the
infrastructure. Thanks to the provided safety guarantees, it is possible to
appropriately adjust the ego-vehicle speed to maintain a proper safety margin.
It is also argued that the framework is suitable for handling sensor failures
and false detections in a systematic way. Three scenarios of growing
information complexity are considered throughout the study. Simulations show
how the increased situational awareness allows the ego-vehicle to maintain a
reasonable speed without sacrificing safety.

Systems and Control;Robotics;Systems and Control