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

TL;DR

This paper introduces Self-Supervised Traffic Advisors (SSTA), infrastructure devices that use video prediction and communication to forecast traffic flow in smart cities, enhancing autonomous vehicle perception and scalability.

Contribution

The work presents a novel infrastructure edge device framework combining self-supervised video prediction with communication for scalable, adaptive traffic forecasting in smart city environments.

Findings

Enables high-quality traffic predictions through device communication

Supports scalability to many devices in a city-wide deployment

Provides lifelong online learning for adaptability to changing traffic conditions

Abstract

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.