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Connected and Autonomous Vehicles (CAVs) are becoming more widely deployed,but it is unclear how to best deploy smart infrastructure to maximize theircapabilities. One key challenge is to ensure CAVs can reliably perceive otheragents, especially occluded ones. A further challenge is the desire for smartinfrastructure to be autonomous and readily scalable to wide-area deployments,similar to modern traffic lights. The present work proposes the Self-SupervisedTraffic Advisor (SSTA), an infrastructure edge device concept that leveragesself-supervised video prediction in concert with a communication andco-training framework to enable autonomously predicting traffic throughout asmart city. An SSTA is a statically-mounted camera that overlooks anintersection or area of complex traffic flow that predicts traffic flow asfuture video frames and learns to communicate with neighboring SSTAs to enablepredicting traffic before it appears in the Field of View (FOV). The proposedframework aims at three goals: (1) inter-device communication to enablehigh-quality predictions, (2) scalability to an arbitrary number of devices,and (3) lifelong online learning to ensure adaptability to changingcircumstances. Finally, an SSTA can broadcast its future predicted video framesdirectly as information for CAVs to run their own post-processing for thepurpose of control.

Self-Supervised Traffic Advisors: Distributed, Multi-view Traffic Prediction for Smart Cities