During these 2 years of project the FAIR consortium has worked to fulfil the scope of the project, finalizing the activities on going and leading an overall achievement of the objectives foreseen.
The main results achieved are presented below:
Crash management flow
The crash management flow developed in the AIR platform allows the analysis of the crash events to detect real crashed occurred on test cars. A trace of the event, the Crash Report, would be generated to describing the impact recorded, the driving behaviour kept 10 mins before the crash with map positioning and the occurrence of diagnostic events after the crash.
Dealers, through the Mydesk, will be allowed to manage issues and receive notification of the occurrence of a crash on one of the car sold through them. By managing the notification, dealer can retrieve the vehicle and plan the recovery interventions needed. MyDesk can be considered as an advanced CRM platform that enhance the marginality and efficiency of of the dealer.
The Software Data Management Platform
The Data management platform, which oversees ingesting, storing, processing, and sharing data collected from IoT external sources to the other FAIR platform modules and to third party entities.
The platform is structured in several layers, the following main areas can be identified:
− Ingestion layer,
− Real time processing layer,
− Batch processing layer,
− Application layer,
− Business intelligence layer,
FAIR Platform and Government Facilities
Air started a collaboration with Lombardia and Piemonte regions on a project that aims to limit the circulation of the most polluting cars through a mileage threshold. Air is proposing to the Lombardia Region reports on the Co2 distribution based on connected cars.
Tariff algorithm
Tariff algorithm has been released a first iteration of dynamic MTPL tariff pricing based on location data (via ZIPCODE) and the inclusion in the tariff algorithm of a subset of the KBIs developed within the Data Management platform.
Behavioral machine learning
ML techniques have been applied to the Dongle data to get a segmentation of the drivers according to the driving style detected, analysing the single trips recorded and detect extreme driving styles considering as discriminating variables the extreme driving events, the duration of the events and the speed recorded.
The algorithm other output is the definition of a classifier that can classify easily the new trips coming from the devices without reperforming the whole process.
In order to have a more reliable Behavioral algorithm it is mandatory a study related to the capacity of the data to describe the risk exposure of the driver and define a link between the KBI and the risk of crash.
Digital process and Master Data Management
The FAIR project business model concern 2 main components:
• Insurance services
• IOT Service for partners and final users (driver) based on connected car data
A new business model based on a subscription economy pricing and billing concept will be implemented to sell IoT services and setting up automated billing schedules on a recurring base.
Air’s offering related to AUTOMOTIVE IoT SERVICES is based on 5 components:
1) Type of client
2) Type of product
3) Additional services:
4) On demand services
5) Payment plans
Billing and Accounting
FAIR aim to a subscription model by automating payment transactions and managing subscription-based services for its customers allowing:
• automate recurring operations
• recalculate recurring revenue streams
• reliable financial cashflow forecasting
