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