A Blockchain Based Liability Attribution Framework for Autonomous Vehicles

TL;DR

This paper proposes a blockchain-based framework for autonomous vehicle liability attribution, ensuring tamper-proof evidence collection and secure interaction recording among involved entities to improve accident adjudication.

Contribution

It introduces a permissioned blockchain model tailored for autonomous vehicle liability, detailing data management, access control, and security analysis to address current challenges.

Findings

Framework provides tamper-proof evidence for liability attribution.

Security analysis confirms resilience against identified attacks.

Partitioned blockchain enables tailored data access for participants.

Abstract

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.