## 1 Introduction  
\"Data is the new oil\" or \"Information is the oil of the 21 st century, and analytics is the combustion engine\" are breakthrough analogical phrase notions adopted by various organizations like Apple, Amazon, Facebook, Alphabet or automotive innovators like Waymo, Tesla, Renault as they embark the digital age of autonomous driving (ADAS). ADAS systems learn and predict using data generated from a fleet of vehicles where each autonomous vehicle can generate up to 300 Terabytes of data per year comprising of different sensors like Radar, Light Detection and Ranging (LIDAR), Camera, Ultrasonic, Vehicle motion, Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU).  
These positive connotations require a certain prudence as data need the five Vs of Volume, Velocity, Variety, Veracity and Value and the three As of Analytics, Algorithms and Applications. These have been well argued, as with the explosion of data by vehicles, computing devices, or the Internet of Things, the data need to have practice. These data practices or management needed are  
* Data Provenance: Knowledge of the origin of data to generate insights and void bias.
* Data Privacy: Right to access, rectification, erasure, processing and portability guaranteed inside the European Union by the General Data Protection and Regulations (GDPRs) while handling data.
* Data Protection: Securing the data is essential as it comes at a high cost if availability is not maintained as earlier; even the Toy Story Movie Franchise had the risk of becoming obsolete when a technician accidentally deleted it.
* Data Preparation: It is necessary to clean insight extraction to make it AI-usable as data quality need to be augmented.  
To ensure the properties mentioned above and the practices, the data need to be incentivized and adopt a decentralized technology like blockchain where multiple stakeholders like data creators, handlers, exploiters and aggregators are involved. In our use case, we can consider the data creators to be vehicles or vehicle propriators, data handlers to be Original Equipment Manufacturers like Renault, data exploiters like Autonomous Driving Solution Providers like Waymo, Wejo, Momenta or Oxbotica, as well as mobility data aggregators like DIMO a blockchain-based solution, Otonomo, Carscan.  
The major contributions of this paper are as follows:  
* A data monetization architecture based on bidding and guaranteeing data privacy is proposed, incentivizing the data creator being a vehicle, the intermediary being the vehicle OEM and finally the data consumer being the radar OEM.
* Ensuring the credibility and the validity of the data with a review system enabled through blockchain smart contracts.
* A new blockchain platform, Substrate, is chosen for implementing the solution which has the features of embedded Smart Contracts, hybrid consensus of block proposal and finalization algorithms.
* The solution is evaluated for its performance on a cloud platform to measure its throughput, consensus forks, and finality of the transactions.  
The paper is organized as follows:  
* The first part introduces data monetization and its importance as well as different commercial decentralized mobility solutions.
* Next, we follow with the state of the art on data monetization and evaluation as well as comparison for each of the works. It is followed by a discussion of different mobility data standards.
* We then continue with our data monetization architecture, blockchain platform, and hybrid consensus. We continue with the data monetization use-case definition, architecture proposal and implementation details on the cloud infrastructure.
* We then end with the functional, performance evaluation with more focus on the hybrid consensus algorithms and a conclusion with future work proposals.