  The tremendous hype around autonomous driving is eagerly calling for emerging
and novel technologies to support advanced mobility use cases. As car
manufactures keep developing SAE level 3+ systems to improve the safety and
comfort of passengers, traffic authorities need to establish new procedures to
manage the transition from human-driven to fully-autonomous vehicles while
providing a feedback-loop mechanism to fine-tune envisioned autonomous systems.
Thus, a way to automatically profile autonomous vehicles and differentiate
those from human-driven ones is a must. In this paper, we present a
fully-fledged framework that monitors active vehicles using camera images and
state information in order to determine whether vehicles are autonomous,
without requiring any active notification from the vehicles themselves.
Essentially, it builds on the cooperation among vehicles, which share their
data acquired on the road feeding a machine learning model to identify
autonomous cars. We extensively tested our solution and created the NexusStreet
dataset, by means of the CARLA simulator, employing an autonomous driving
control agent and a steering wheel maneuvered by licensed drivers. Experiments
show it is possible to discriminate the two behaviors by analyzing video clips
with an accuracy of 80%, which improves up to 93% when the target state
information is available. Lastly, we deliberately degraded the state to observe
how the framework performs under non-ideal data collection conditions.
