Chuka Oham, Salil S. Kanhere, Raja Jurdak, Sanjay Jha
The advent of autonomous vehicles is envisaged to disrupt the auto insurance\nliability model.Compared to the the current model where liability is largely\nattributed to the driver,autonomous vehicles necessitate the consideration of\nother entities in the automotive ecosystem including the auto\nmanufacturer,software provider,service technician and the vehicle owner.The\nproliferation of sensors and connecting technologies in autonomous vehicles\nenables an autonomous vehicle to gather sufficient data for liability\nattribution,yet increased connectivity exposes the vehicle to attacks from\ninteracting entities.These possibilities motivate potential liable entities to\nrepudiate their involvement in a collision event to evade liability. While the\ndata collected from vehicular sensors and vehicular communications is an\nintegral part of the evidence for arbitrating liability in the event of an\naccident,there is also a need to record all interactions between the\naforementioned entities to identify potential instances of negligence that may\nhave played a role in the accident.In this paper,we propose a BlockChain(BC)\nbased framework that integrates the concerned entities in the liability model\nand provides untampered evidence for liability attribution and adjudication.We\nfirst describe the liability attribution model, identify key requirements and\ndescribe the adversarial capabilities of entities. Also,we present a detailed\ndescription of data contributing to evidence.Our framework uses permissioned BC\nand partitions the BC to tailor data access to relevant BC\nparticipants.Finally,we conduct a security analysis to verify that the\nidentified requirements are met and resilience of our proposed framework to\nidentified attacks.\n