  Multiple studies have illustrated the potential for dramatic societal,
environmental and economic benefits from significant penetration of autonomous
driving. However, all the current approaches to autonomous driving require the
automotive manufacturers to shoulder the primary responsibility and liability
associated with replacing human perception and decision making with automation,
potentially slowing the penetration of autonomous vehicles, and consequently
slowing the realization of the societal benefits of autonomous vehicles. We
propose here a new approach to autonomous driving that will re-balance the
responsibility and liabilities associated with autonomous driving between
traditional automotive manufacturers, infrastructure players, and third-party
players. Our proposed distributed intelligence architecture leverages the
significant advancements in connectivity and edge computing in the recent
decades to partition the driving functions between the vehicle, edge computers
on the road side, and specialized third-party computers that reside in the
vehicle. Infrastructure becomes a critical enabler for autonomy. With this
Infrastructure Enabled Autonomy (IEA) concept, the traditional automotive
manufacturers will only need to shoulder responsibility and liability
comparable to what they already do today, and the infrastructure and
third-party players will share the added responsibility and liabilities
associated with autonomous functionalities. We propose a Bayesian Network Model
based framework for assessing the risk benefits of such a distributed
intelligence architecture. An additional benefit of the proposed architecture
is that it enables "autonomy as a service" while still allowing for private
ownership of automobiles.
