Researchers at the Department of Energy’s Oak Ridge National Laboratory have developed a deep learning algorithm that analyzes drone, camera, and sensor data to reveal unusual vehicle patterns that may indicate illicit activity, including the movement of nuclear materials. The work is published in the journal Future Transportation.

The software monitors routine traffic over time to establish a baseline for “patterns of life,” enabling detection of deviations that could signal something out of place. For example, a surge in overnight truck traffic at a facility which is normally only visited during the day could reveal illegal shipments.

The research builds on a previous ORNL-developed technology for recognizing specific vehicles from side views. Researchers improved the structure of this software’s deep learning network to provide much broader capabilities than any existing recognition systems, said ORNL’s Sally Ghanem, lead researcher.