  In this paper, we demonstrate a proof of concept for characterizing vehicular
behavior using only the roadside cameras of the ITS system. The essential
advantage of this method is that it can be implemented in the roadside
infrastructure transparently and inexpensively and can have a global view of
each vehicle's behavior without any involvement of or awareness by the
individual vehicles or drivers. By using a setup that includes programmatically
controlled robot cars (to simulate different types of vehicular behaviors) and
an external video camera set up to capture and analyze the vehicular behavior,
we show that the driver classification based on the external video analytics
yields accuracies that are within 1-2\% of the accuracies of direct
vehicle-based characterization. We also show that the residual errors primarily
relate to gaps in correct object identification and tracking and thus can be
further reduced with a more sophisticated setup. The characterization can be
used to enhance both the safety and performance of the traffic flow,
particularly in the mixed manual and automated vehicle scenarios that are
expected to be common soon.
