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!