  The advent of autonomous vehicles is envisaged to disrupt the auto insurance
liability model.Compared to the the current model where liability is largely
attributed to the driver,autonomous vehicles necessitate the consideration of
other entities in the automotive ecosystem including the auto
manufacturer,software provider,service technician and the vehicle owner.The
proliferation of sensors and connecting technologies in autonomous vehicles
enables an autonomous vehicle to gather sufficient data for liability
attribution,yet increased connectivity exposes the vehicle to attacks from
interacting entities.These possibilities motivate potential liable entities to
repudiate their involvement in a collision event to evade liability. While the
data collected from vehicular sensors and vehicular communications is an
integral part of the evidence for arbitrating liability in the event of an
accident,there is also a need to record all interactions between the
aforementioned entities to identify potential instances of negligence that may
have played a role in the accident.In this paper,we propose a BlockChain(BC)
based framework that integrates the concerned entities in the liability model
and provides untampered evidence for liability attribution and adjudication.We
first describe the liability attribution model, identify key requirements and
describe the adversarial capabilities of entities. Also,we present a detailed
description of data contributing to evidence.Our framework uses permissioned BC
and partitions the BC to tailor data access to relevant BC
participants.Finally,we conduct a security analysis to verify that the
identified requirements are met and resilience of our proposed framework to
identified attacks.
