  Autonomous driving is expected to provide a range of far-reaching economic,
environmental and safety benefits. In this study, we propose a fog computing
based framework to assist autonomous driving. Our framework relies on overhead
views from cameras and data streams from vehicle sensors to create a network of
distributed digital twins, called an edge twin, on fog machines. The edge twin
will be continuously updated with the locations of both autonomous and
human-piloted vehicles on the road segments. The vehicle locations will be
harvested from overhead cameras as well as location feeds from the vehicles
themselves. Although the edge twin can make fair road space allocations from a
global viewpoint, there is a communication cost (delay) in reaching it from the
cameras and vehicular sensors. To address this, we introduce a machine learning
forecaster as a part of the edge twin which is responsible for predicting the
future location of vehicles. Lastly, we introduce a box algorithm that will use
the forecasted values to create a hazard map for the road segment which would
be used by the framework to suggest safe manoeuvres for the autonomous vehicles
such as lane changes and accelerations. We present the complete fog computing
framework for autonomous driving assist and evaluate key portions of the
proposed framework using simulations based on a real-world dataset of vehicle
position traces on a highway
