BACKGROUND OF THE INVENTION

The present invention relates generally to the field of information and communication technology, and more particularly to identifying autonomous vehicles contributing to traffic congestion.

In recent years, developments in advanced controls systems have provided autonomous vehicles to operate safely with little or no human input. An autonomous vehicle is a vehicle capable of sensing a surrounding environment and operating without human involvement. Additionally, a human passenger is not required to take control of the vehicle at any time, nor is a human passenger required to be present in the vehicle at all. Autonomous vehicles combine a variety of sensors to perceive the surroundings environment, such as radar, lidar, sonar, global positioning system (GPS), odometry, and inertial measurement units. Autonomous vehicles utilize advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage.

A Smart city is an urban area that uses different types of electronic Internet of Things (IoT) sensors to collect data and use insights gained from that data to manage assets, resources and services efficiently. This includes data collected from citizens, devices, and assets that is processed and analyzed to monitor and manage traffic and transportation systems. The smart city concept integrates information and communication technology (ICT), and various physical devices connected to the IoT network to optimize the efficiency of city operations and services and connect to citizens. Smart city technology allows city officials to interact directly with both community and city infrastructure and to monitor what is happening in the city and how the city is evolving.

SUMMARY

Aspects of the present invention disclose a method, computer program product, and system for identifying autonomous vehicles that are causing traffic congestion due to anticipation of a pick-up event. The method includes one or more processors obtaining data of one or more sensors of an autonomous vehicle. The method further includes one or more processors obtaining video data of a location that includes the autonomous vehicle. The method further includes one or more processors determining a route of the autonomous vehicle based at least in part on the video data of the location that includes the autonomous vehicle. The method further includes one or more processors determining whether the autonomous vehicle is performing a traffic infraction based at least in part on the route of the autonomous vehicle.

