A Blockchain Based Liability Attribution Framework for Autonomous Vehicles
The advent of autonomous vehicles is envisaged to disrupt the auto insurance liability model. Unlike the current model, wherein in the event of an accident, the liability is largely attributed to the driver, autonomous vehicles necessitate the consideration of other entities in the automotive ecosystem such as the auto manufacturer, software provider, service technician and the vehicle owner for liability attribution. 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 such as an automaker with significant understanding of the inner workings of the autonomous vehicle or the vehicle owner with unrestricted access to the internal network of the vehicle. 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. Furthermore, autonomous vehicle sensors are envisaged to collect significant amount of data including personal identifiable information about a vehicle owner which threatens his privacy. 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 adjuducation. We first describe the liability attribution model, identify key requirements for our proposed BC-based framework and describe the adversarial capabilities of entities. Also, we present a detailed description of relevant data contributing to evidence and further describe how they are stored and used as evidence. Our framework uses permissioned BC, to restrict access to relevant entities and partitions the BC to further 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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