基于区块链的自动驾驶汽车责任归属框架
A Blockchain Based Liability Attribution Framework for Autonomous Vehicles
摘要：
预计自动驾驶汽车的出现将打破汽车保险责任模式。与目前的模式相比，责任主要归因于驾驶员，自动驾驶汽车需要考虑汽车生态系统中的其他实体，包括汽车制造商，软件提供商，自动驾驶汽车中传感器和连接技术的激增使自动驾驶汽车能够收集足够的数据用于责任归属，但增加的连接性使车辆暴露于来自交互实体的攻击。这些可能性促使潜在的责任实体否认其参与碰撞事件以逃避责任。虽然从车辆传感器和车辆通信收集的数据是事故发生时仲裁责任的证据的组成部分，但也需要记录上述实体之间的所有互动，以确定可能在事故中发挥作用的潜在疏忽情况。我们提出了一个基于区块链（BC）的框架，该框架将相关实体集成到责任模型中，并为责任归属和裁决提供未经篡改的证据。我们首先描述了责任归属模型，确定关键要求并描述实体的对抗能力。此外，我们提出了一个详细的描述数据有助于evidence.Our框架使用许可BC和分区BC定制数据访问相关的BC participator.Finally，我们进行了安全分析，以验证所确定的要求得到满足和弹性我们提出的框架，以确定攻击。
英文摘要：
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.
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