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Create data-driven services from vehicle operating data.

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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)

Publicado en: Datos y análisis
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Create data-driven services from vehicle operating data.

  1. 1. Create data-driven services from vehicle operating data Findings from the projects AEGIS and EVOLVE Alexander Stocker alexander.stocker@v2c2.at
  2. 2. • 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
  3. 3. Vehicle Operation Data Other contextual data E.g. Weather data, map data, .. Data-driven Services Automotive Data Science
  4. 4. 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
  5. 5. 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
  6. 6. • 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
  7. 7. 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
  8. 8. 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)
  9. 9. 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
  10. 10. 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)
  11. 11. 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
  12. 12. 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
  13. 13. Proof-of- Concepts on EVOLVE testbed
  14. 14. TripDataVisualiser Running in a Docker container environment Shiny Server (R Studio) Database PostgreSQL + Timescale + PostGIS Event Extraction R Distribution (algorithms) I II
  15. 15. Demo: Trip Data Visualizer http://insv02153.v2c2.at:9004/
  16. 16. 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
  17. 17. EVOLVE testbed
  18. 18. Backup
  19. 19. Backup
  20. 20. Backup

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