A Vision of Smart Traffic Infrastructure for Traditional, Connected, and Autonomous Vehicles

This smart city traffic management approach seeks to use edge-based video-stream processing (using multicore and GPU processors) at intersections and in public vehicles (city buses, fire trucks, ambulances, school buses) to convert video data into space-time trajectories of individual vehicles and pedestrians that are transmitted to a cloud-based system. Key information is then synthesized in the cloud from them to create a real-time city-wide traffic palette. Real-time or offline processing both at the edge and the cloud will then be leveraged to optimize intersection operations, manage network traffic, identify near-collisions between various units of traffic, provide street parking information, and a host of other applications. Additional information such as weather and environment will also be leveraged. The use of edge-based real-time machine learning (ML) techniques and videostream processing has several significant advantages. (1) Because there is no need to store copious amounts of video (few minutes typically suffice for edge-based processing), it automatically addresses concerns of public agencies who do not want person-identifiable information to be stored for reasons of citizen privacy and legality. (2) The processing of the video stream at the edge will allow for the use of low bandwidth communication using wireline and wireless networks to a central system such as a cloud, resulting in a compressed and holistic picture of the entire city. (3) The real-time nature of processing enables a wide variety of novel transportation applications at the intersection, street, and system levels that were not possible hitherto, significantly impacting safety and mobility.