VSS Data Project

Objective

To develop an open graph server exposing real world data represented with the Vehicle Signals Specification (VSS) data model.

We want to demonstrate its ready usability with something tangible people can try for themselves, encouraging uptake with prospective data consumers and producers alike.

The W3C Auto WG sees the need for harmonizing this information not only within vehicles but in the cloud and with other standards efforts. We want to ensure the data we are exposing in-vehicle and bringing to the cloud fits into a broader transportation data marketplace. We want to promote consistency of vehicle data, on-board (VSS via VISS or 'Gen2') and in the cloud (VSSo - VSS ontology).

Information is a commodity but its value is diminished if it lacks metadata or uses a proprietary (OEM specific) data structure requiring custom development to use and attempt to align with other data sources (mixed fleet). We want to show it is feasible to establish a cross-manufacturer data marketplace.

Policy

Participation in providing data is limited to individuals and organizations in the W3C Automotive who have agreed to terms outlined in the data policy.

Background

W3C has a working data model for vehicle signals dubbed VSS, and BMW has already mapped legacy vehicles signals to VSS. Volvo will be or possibly is shipping production vehicles with VISS implementation, exposing VSS within the vehicle. BMW has also demonstrated applications using GraphQL. BMW internally uses GraphQL to create a single access layer for data coming from multiple sources such as databases, REST, and signals from millions of cars. The VW submission’s ViWi protocol aims to expose Graph.

Project Description

To demonstrate to the automotive community (OEMs and developers) the benefit of adopting the VSS data model several organizations participating in W3C Auto WG have demonstrated initial interest, and will create/demonstrate applications using GraphQL, based on interoperability using VSS (cloud based). The intent is for W3C members to present this to the global auto community through international conferences.

High level project roadmap

Create a list of analytics use cases, including required signals in this wiki.
Decide on a few to focus on based on potential ability to acquire signals, interest etc.
Graph engine selection, installation.
Define a common understanding on appropriate rules, policies, and practices related to what is required for data to be anonymous, not to breach GDPR, or other similar requirements.
Select an appropriate internal sets of data related to the selected use cases, and applies the practices from the previous step.
Transform provided data sets from their proprietary format to the VSS.
Agree on other common parameters helpful for smooth interoperability, such as time stamp format, data base structures, file formats, etc.
Upload the transformed data to the MIT server.
Work on analytical queries and tools on the aggregated data lake according to the selected use cases.
Present results to the automotive community at conferences.

Mapping exercise

To learn internally how to implement the mapping of signals between current proprietary data representations and the VSS data model, select a few signals that are likely to be part of the use cases selected by the project, and perform a mapping of these.

Data input

Participants will provide vehicle data in the VSS format to a common server, where it would be open for analysis. A few use cases will be defined in order for the participants to know what vehicle data to bring to the common server. It is expected that the data is anonymized before brought to the common server, and that it does not breach policy rules like GDPR, etc. The results from applying analytics to this accumulated set of data from multiple participants can then be presented to the global automotive community at conferences or similar.

Server for the data aggregation

W3C/MIT will set up a server where the aggregated data from the participants can be stored. More info on this will follow.

Use Cases

Below follows a list of proposed analytics use cases. The project needs to decide on which to use, in order for the participants to start working on translating from their proprietary formats to the VSS format.

Slippery road - ABS brake incidence
- ABS activation data + GPS
Dangerous road/intersection - harsch braking/acceleration
- Accelerometer data + GPS
Micro weather
- Wiper activation, outside temperature, ABS
Predicitve maintenance
- Battery cranking voltage (100 Hz - during cranking only), engine oil temperature, ambient temperature
Pothole detection
- Accelerometer data
Traffic volume for traffic planning
- GPS, speed
Traffic routes
- GPS, speed
First responder data - vehicle data that may help first responders to get a beforehand understanding of the incident
- Door lock/unlock status, seat hasPassenger/isBelted, ...
Carbon footprint calculator
- speed, fuel level, kilometers, ambient temperature, wiper sensor, altitude, passengers (typically only have passenger sensors in front seats)
Road Worthiness
- tire pressure, oil level, coolant level, fuel level or battery status

Further use cases are welcome to be added to this list.

Common Signals

Consider defining DGGS shapes for regions instead of longitude/latitude for privacy considerations.

If you add signals to use cases above, ensure they are reflected here

ABS activation

Accelerometer

GPS

GPS - altitude only, not long/lat

Fuel/charge level

Speed

Odometer (trip less identifiable than overall or send fuel efficiency calculation instead)

Wiper activation

Engine oil temperature

Coolant level

Oil pressure

Tire pressure

Ambient temperature

Door lock status

Seatbelt status

Seated Passenger detection

Vss data