Create data-driven services from vehicle operating data. Findings from the projects AEGIS and EVOLVE Alexander Stocker (Key Researcher & Project Manager, Virtual Vehicle Research Center)

2. Create data-driven
services from vehicle
operating data
Findings from the projects
AEGIS and EVOLVE
Alexander Stocker
alexander.stocker@v2c2.at

3. • Car related revenues will decline in the long-run, data-driven services will
overcompensate after 2050 (Source: Accenture 2018)
• The overall revenue pool from car data monetization at a global scale might add
up to USD 450 - 750 billion by 2030 (Source: McKinsey 2016)
Motivation: Digital Transformation of Automotive Industry

4. Vehicle Operation Data
Other contextual data
E.g. Weather data, map data, ..
Data-driven Services
Automotive
Data
Science

5. Advanced Big Data Value
Chain for Public Safety and
Personal Security
“AEGIS brings together the
data, the network and the
technologies to create a
curated, semantically
enhanced, interlinked and
multilingual repository for
“Public Safety and Personal
Security”-related Big Data.”
www.aegis-bigdata.eu
(ICT-14-2016-2017)
I want to know areas of
road damage!
I want to know how to
drive more safely!
I want to know safety-
critical hotspots in my
region/city!
HPC and Cloud-enhanced
Testbed for Extracting Value
from Diverse Data at Large Scale
“Leading the Big Data Revolution
by integrating the High-
Performance Computing, Cloud
and Big Data worlds in a unique
large-scale testbed applied in 7
pilot domains.”
www.evolve-h2020.eu
(ICT-11-2018-2019)
Proof-of-
Concepts
on EVOLVE
testbed
Automotive Demonstrator

6. 1. Create and test
algorithms for inferring
driving style and road
surface quality in vehicle
operation data
2. Port algorithms to
AEGIS platform
3. Used the AEGIS platform
to test and calibrate
algorithms on more
complicated “real” road
data

7. • Vehicle measurement data was collected
with a custom-built logging device
• Raw data was transferred to the platform
“as-is”
• Amount of data:
• 2163 trips from 11 drivers
• Raw data : ~47 GB
• Processed data (including intermediate results
and weather data): ~57GB
• Total demonstrator data: ~104GB

9. Data Pipeline
Processing step:
“Resampling”
• All measurement signals (e.g. acceleration, speed,
gps, ..) are recorded at irregular time intervals
• For each signal, we interpolate the recorded
values (using natural splines)
• Sample the fitted spline at regular time intervals
(10 Hz, 1/10 sec) for easier analysis
Time
…
Time
Time

10. Data Pipeline
Processing step:
“Coordinate system aligning”
• The sensor can have an arbitrary position in the
car, but position of sensor is fixed during trip
• The coordinate system of the sensor does not
coincide with the coordinate system of the vehicle
• On average the vehicle Z-direction coincides with
gravity vector (vehicle drives horizontally)

11. Data Pipeline
Processing step:
“Event extraction and enrichment”
• Within the data we search for certain events that form the basis of further analyses, e.g.:
• Hard braking (safe driving)
• Fast acceleration (safe driving)
• Potholes (road surface quality)
• Extracted events are enriched with weather information
event
vehicle measurements

12. Data Pipeline
Processing step:
“Find damaged road areas“
• Use prepared data
• Detect road damage using z-acceleration and
pitch rotation
• Calculate absolute road damage density using a
kernel density estimator (KDE)
• Normalize density by KDE of GPS positions
Z-acceleration
Z-acceleration
Pitch (gyro)

13. Data Pipeline
Processing step:
“Quantify a person’s driving risk“
• Use prepared data
• Detect safety critical events (harsh acceleration,
harsh braking, …)
• Compute event severity taking weather into
account
• Compute relative risk scores by comparing the
weighted event rates
81%Safe Driving Score

14. Data Pipeline
Processing step:
“Quantify a region’s driving risk“
• Use prepared data
• Detect safety critical events
• Compute event severity taking weather into
account
• Aggregate weighted events by region
Safe Driving Heatmap

15. Proof-of-
Concepts
on EVOLVE
testbed

16. TripDataVisualiser
Running in a Docker
container environment
Shiny Server
(R Studio)
Database
PostgreSQL + Timescale +
PostGIS
Event Extraction
R Distribution (algorithms)
I
II

17. Demo: Trip Data Visualizer
http://insv02153.v2c2.at:9004/

18. EVOLVE testbed
Ease of deployment, access, and use in a
shared manner, while addressing data
protection
An advanced computing
platform with HPC features
and systems software.
A versatile big-data
processing stack for end-to-
end workflows.
1 2
3

19. EVOLVE testbed

21. Backup

22. Backup

23. Backup