  Assessing collision risk is a critical challenge to effective traffic safety
management. The deployment of unmanned aerial vehicles (UAVs) to address this
issue has shown much promise, given their wide visual field and movement
flexibility. This research demonstrates the application of UAVs and V2X
connectivity to track the movement of road users and assess potential
collisions at intersections. The study uses videos captured by UAVs. The
proposed method combines deep-learning based tracking algorithms and
time-to-collision tasks. The results not only provide beneficial information
for vehicle's recognition of potential crashes and motion planning but also
provided a valuable tool for urban road agencies and safety management
engineers.
