baud ii: large scale data collection and analysis for data ... · conclusions • access to high...
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BAuD II: Large Scale Data Collection and Analysis for Data-driven Product
Development
Mathias Johanson
Alkit Communications AB
Scope • How can we collect both subjective user experience data and objective
measurement data from connected vehicles?
• How can we scale up this data collection and make data quality higher?
• How can we analyze subjective and objective data together to increase knowledge about how products are used and experienced?
• How can we improve Active Safety and AD systems (and thereby traffic safety) based on feedback of user experience data and measurement data?
• How can we shorten development cycles by continuous improvements of software, supported by connectivity and telematics services?
• How can we preserve the privacy of users while capturing large volumes of subjective and objective data?
Background: Big Data as an enabler for knowledge-driven product development
Capture
Connectivity, Telematics, Diagnostics
Analyze
Big Data analytics, Data mining,
Machine Learning
Decide
Knowledge bases, data sharing, collaboration
BAuD Framework
KB
Collaboration, decision-making
Collaboration, decision-making
Raw data Information
Knowledge
Other data sources
In-vehicle data sources (WICE)
Need for new knowledge identified
Design subjective & objective data capture tasks
Capture subjective and
objective data
Analyze subjective &
objective data
Improve vehicular software based on analyses and ML
Continuous deployment (in
test vehicle fleets)
WICE in-vehicle data logging & telematics
Smartphone app
Cloud-based analytics framework and methodology
ML training data sets
Rapid prototyping framework
WICE telematics & remote software download
How do customers
experience our products?
How are the vehicular
subsystems performing?
Data capture configuration and survey design tools
Concept
External data sources
Product developer
Was the alert
relevant ?
Yes No
BAuD/WICE back-end
Questions sent to app
In-vehicle signals monitored and logged
Answers uploaded
Driver’s smartphone with subjective data capture app
VCC Engineer
Joint Subjective / Objective
Data Capture Approach
Telematics unit (WICE) Test vehicle
Montrig
Analytics fw
Back-end server architecture
Smartphone App Service Layer
Measurement Task Manager
Presentation layer / User interface
Analytics Framework
Monitoring & Triggering
mechanism
Subjective Data Task Manager
Survey design tool Measurement Task design tool
Users
Data sources
Telematic service layer
Framework Architecture
? ? ?
Data Capture and Wireless
Communication Units
WICE GW
3G/4G/WLAN
WICE Portal web front-end
Test Vehicle
fleet
Database
and file store
Analytics services
In-vehicle
WICE units WICE users
WICE back-end
WICE Data Capture and Telematics Metrology, Fleet Management, Rapid Prototyping, Software Download
Smartphone App Development
Subjective Data Capture
• App can capture data using text-to-speech and voice recognition
when?
Poll Question Types multiple choice yes / no rating
Joint Subjective / Objective Data Analytics
• Capture data (subjective and objective) analyzed in a common framework
• Analysis tasks should be automated
• Data can be used for training of ML algorithms
Privacy and integrity issues
• When subjective data capture is scaled up to large customer groups, privacy issues must be considered
• Approach is to use differential privacy – Noise is added to captured data in a controlled
way, so that it cancels out at analysis stage
• Licentiate thesis: – Boel Nelson, ”Data Privacy for Big Automotive
Data”, 2017.
Telematic service layer
Back-end server architecture
Smartphone App Service Layer
Measurement Task Manager
Presentation layer / User interface
Analytics Framework
Subjective Data Task Manager
Survey design tool Measurement Task design tool
Users
Data sources
Privacy preservation layer
Revised Framework Architecture
? ? ?
Monitoring & Triggering
mechanism
Privacy preservation layer
Pilot Use Cases
• Two focused active safety use cases: Driver Alert (DAC) and Forward Collision Warning (FCW)
DAC Use Case
• Investigate distribution of ’tired’ vs. ’distracted’ – When DAC triggers, ask driver ”Do you feel tired?”
• Response alternatives: YES / NO • If NO, ask ”Were you doing something other than driving when the
alert appeared?” – Response alternatives: YES / NO
• Follow up whether driver takes a pause a suggested – When the car comes to a halt, if DAC has triggered and the
driver answered "Yes“ (is tired), ask driver ” Did you take a break?” • Response alternatives: YES / NO • If NO, ask ” Was this because you: (1) were close to the target
destination, (2) didn't understand the suggestion, (3) didn't feel tired (4) could drive the car without problem?”
FCW Use Case
• FCW – acceptance for false warnings
– When FCW triggers, ask driver ”Did you feel that the collision warning was correct?”
• Response alternatives: YES / NO
• If NO, ask ”Was the collision warning disturbing?” – Response alternatives: YES / NO
Conclusions
• Access to high quality data (both subjective & objective) improves vehicle development (cf. knowledge-driven product development)
• Proof-of-concept implementation shows that subjective and objective data can be captured and analyzed together to improve data quality
• Supports Rapid Prototyping of new in-vehicle functions and services
• System can be used to capture training data sets for Machine Learning algorithms in Active Safety and AD systems
• Supports Continuous Deployment of software in test vehicle fleets • Improved connected active safety and AD systems improves traffic
safety • Contributes heavily to digitization, leveraging IoT, ML, Big Data and
Cloud Computing technology for vehicular applications
Thank you!