Cameras at intersections and other busy roads are not new. They enable traffic monitoring and provide images in case of accidents. In the future, the camera shots could be even more valuable – with the help of Artificial Intelligence. The evaluation of traffic data by supercomputers could soon ensure that roads will become safer. By evaluating data and analysing traffic in a short time. Scientists from the Texas Advanced Computing Center (TACC), the Center for Transportation Research at the University of Texas and the Texan city of Austin are working on programs that use deep learning and data mining to make roads safer and eliminate traffic problems.

The scientists are developing Artificial Intelligence that uses deep learning to evaluate video recordings of traffic points. This software should be able to recognise and classify objects correctly. The objects are cars, buses, trucks, motorcycles, traffic lights and people. The software determines how these objects move and behave. In this way, information can be gathered that can be analysed more precisely to prevent traffic problems. The aim is to develop software to help transport researchers evaluate data. The Artificial Intelligence should be flexible in its use and be able to recognise traffic problems of all kinds in the future without anyone having to program them explicitly for this purpose.

Thanks to deep learning, supercomputers classify the objects correctly and estimate the relationship between the detected objects in road traffic by following the movements of cars, people, etc. After this work was done, the scientists gave the software two tasks: Count the number of vehicles driving along a road. And more difficult: Record near collisions between cars and pedestrians. The Artificial Intelligence scored 10 minutes of video footage and counted all vehicles with 95% security. Being able to measure traffic accurately is a valuable skill of the supercomputer. At present, numerous expensive sensors are still needed to obtain such data, or studies must be carried out which would only produce specific data. The software, on the other hand, can monitor the volume of traffic over an extended period and thus provide figures on traffic volumes that are far more accurate. This procedure makes it possible to make better decisions on the design of road transport.

In the case of near collisions, the Artificial Intelligence enabled scientists to identify situations where pedestrians and vehicles were approaching threateningly automatically. Thus, it possible to determine points of traffic that are particularly dangerous before accidents happen. The data analysed could prove very revealing when it comes to eliminating future traffic problems.

The next project: The software will learn where pedestrians cross the road, how drivers react to signs pointing out pedestrians crossing the street, and how far they are willing to walk to reach the pedestrian path. The project by the Texas Advanced Computing Center and the University of Texas illustrates how deep learning can help reduce the cost of analysing video material.