AVChain: Trusted Sharing of Autonomous Vehicle Crash Incident Data using Interoperating HyperLedger Fabric Networks and IPFS

Autonomous vehicles (AVs) are gaining in popularity over the years as a viable cab service apps as well as for personal use. However, incidents of crashes involving AVs continue to occur, adversely affecting their prospects for widespread acceptance by both end users and regulatory authorities. While such cases are routinely investigated, in the absence of a human to testify on what caused the crash, one has to rely solely on available data. It is therefore imperative that the data logged by AVs is accessible to the concerned parties in a trustworthy manner. In this paper, we present AVChain - a novel framework for using a permissioned blockchain like HyperLedger Fabric (HLF) to record and share AV data comprised of sensors, actuators, maps, planning algorithms and machine learning models so that the data stays immutable even in the face of cross blaming among involved parties. Since the data volume is extremely large, we appropriately compress and down sample the same before storing in a distributed file system, namely, IPFS (Inter-Planetary File System). The hashes of such IPFS data called Content Ids (CIDs) are committed to the HLF network for making them tamper proof. The HLF ledger can later be queried to obtain the CIDs, which are then further used to retrieve and un-compress the original data from IPFS. Effectiveness and usability of AVChain is demonstrated by generating autonomous vehicle data from CARLA, which is a widely used open source AV simulator. For sharing AV data across organizations like sensor and actuator suppliers, map service providers, machine learning model developers and law enforcement authorities, the Weaver tool has been used to make multiple HLF networks interoperate. We have also developed a web application to demonstrate the working of AVChain. Results of an extensive set of experiments establish the efficacy of our approach.