Using UAVs for vehicle tracking and collision risk assessment at intersections
This work addresses traffic safety management for urban road agencies and safety engineers, but it is incremental as it combines existing deep-learning tracking and time-to-collision methods with UAV deployment.
This research tackled the problem of assessing collision risk at intersections by using UAVs and V2X connectivity to track road users and evaluate potential collisions, resulting in a tool that provides beneficial information for vehicle recognition and motion planning.
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.