Insurers' journeys to build a mastery in the IoT usage
Aria Etemad, Ford, euroFOT: European large-scale Field Operational Test on Active Safety Systems
1. euroFOT:
European Large Scale Field Operational
Test on In-Vehicle Systems
Bringing Intelligent Vehicles to the Road
Giancarlo Alessandretti, Angelos Amditis,
Aria Etemad, Christoph Kessler
Stockholm, ITS 2009
www.eurofot-ip.eu
2. Contents
• The euroFOT project
Objectives, functions, expected results
• Highlights for on-going activities
Research hypotheses
Data acquisition
Design of experiment
Operation of Vehicle Management Centres
Data analysis plan
• Lesson learnt and conclusions
2 ITS 2009 Stockholm www.eurofot-ip.eu
3. Why EuroFOT?
• Intelligent Vehicles have a large potential for safer,
cleaner and more efficient transport solutions
• Technologies are mature (several examples in
series production)
• However, we have limited data on real-life operation
and how drivers use the systems in ordinary traffic
• This restricts our capabilities to improve the
systems, understand their impacts and make the
right decisions for deployment
3 ITS 2009 Stockholm www.eurofot-ip.eu
4. euroFOT Objectives
• Perform multiple coordinated tests of Intelligent
Vehicle Systems with ordinary drivers in real
traffic
• Investigate performance, driver behaviour and user
acceptance
• Assess the impacts on safety, efficiency and the
environment, based on road data
4 ITS 2009 Stockholm www.eurofot-ip.eu
5. euroFOT Objectives (2)
• Consolidate a common European approach for FOTs
• Improve public awareness on the potentialities of
driver support functions
5 ITS 2009 Stockholm www.eurofot-ip.eu
7. Exposures in different FOTs
Volvo trucks
16
FOT duration (months)
trucks
Freightliner
2001 cars
100 car EuroFOT
2004
12 Mach
2005 euroFOT-
trucks only
8
Australia
2005 Isa_UK
2007
Ass.driver
NL_2006
4
Within euroFOT:
Within euroFOT:
~18,5 Mil. km
~18,5 Mil. km ACAS
RDCW – 2005
2004 ICC 1997
0
1 10 100 1000
Nr. vehicles
7 ITS 2009 Stockholm www.eurofot-ip.eu
8. The challenges....
• Wide range of driving conditions
• Extraction of meaningful use cases / driving scenarios
for the equipment under test
• Transferable and harmonized methods Europe-wide
• Data quality assurance
• Privacy and legal issues
8 ITS 2009 Stockholm www.eurofot-ip.eu
9. .... and the euroFOT approach
• Well developed ADAS and sensors on vehicles
• Reliable Data Acquisition systems based on partners’
experience
• Harmonised research questions / hypotheses for all
fleets
• Common definition of scenarios
and performance indicators:
specific approaches when
necessary
• Framework of guidelines for the
operation of the different Vehicle
Management Centres
9 ITS 2009 Stockholm www.eurofot-ip.eu
12. Harmonised requirements for systems
and functions
The FESTA methodology has been applied:
• Approx. 250 hypotheses examined
• 84 hypotheses prioritised
• Main relevance: 44% safety, 46% driver, 10% environment
• 5 Combinations of functions
12 ITS 2009 Stockholm www.eurofot-ip.eu
13. Research Questions & Hypotheses
(example)
• A research question is general, and has a question mark:
Does FCW decrease incidents?
• Hypotheses are either true or false
FCW reduces occurence of decelerations above 5 m/s2
• Hypotheses can only be
tested by means of
reasonable indicators
• Hypotheses are statistically
testable
• euroFOT examined >100
hypotheses for FCW/ACC
(only top-hypotheses selected)
13 ITS 2009 Stockholm www.eurofot-ip.eu
14. Data Acquisition Systems
Three types of Data Acquisition Systems are considered:
• CAN only basic functionality for vehicle data and GPS
• CAN + video cameras will record both the road and
the driver
• CAN + video + extra sensors some vehicles equipped
with radar/lidar, head/eye trackers and other sensing
devices
GPS & GPRS Antennas multipurpose camera
OR
data logger
accelerometer
ECU OBD ECU radar
connector
Vehicle CAN bus
ECU ECU
Standard with vehicle
Mandatory part of the instrumentation (data)
Optional part of the instrumentation
14 ITS 2009 Stockholm www.eurofot-ip.eu
15. Data Acquisition Systems
Some specific issues:
• Data enrichment by maps
• Protection of proprietary data
• Encryption of tha data
• Driver annotation interface
• Possibility of audio recording
• Triggered logging
• Synchronisation with GPS
15 ITS 2009 Stockholm www.eurofot-ip.eu
16. Design of experiment
Drivers
•Represent as much as possible the population of buyers
•Professional drivers in the case of trucks
•Perfomance compared with and without the system (Baseline)
•Control group introduced for several functions
Vehicles
•According to the present EU market, technical feasibility and interests
of manufacturers
•Middle+top class cars, heavy trucks
Environment
• Specific events defined (e.g: overtaking manoeuvre)
• Situation variables specified (e.g: weather, visibility, driver
status)
• Performance indicators are the basis for the overall evaluation
(e.g: mean speed, mean time headway, frequency of
braking,…)
16 ITS 2009 Stockholm www.eurofot-ip.eu
17. Events and situation variables
weather
(e. g. rain)
traffic
overtaking (e. g. low volume)
Events manoeuvre
lighting
(e. g. day light)
road type
(e. g. rural road)
Scenario
overtaking in day light in low
vehicle occupancy
traffic on rural road …
(e. g. 1 passenger)
For euroFOT:
• 25 events selected / 27 situation variables
• 81 performance indicators
• About 110 corresponding measures
17 ITS 2009 Stockholm www.eurofot-ip.eu
18. VMC Operations Center
Some of the aspects which required a special attention:
Operation
Operation
Maintenance
Maintenance Sales
Sales Center
Center
Contract + HOT LINE
+ HOT LINE
Data/Operational Consent form Training
Quality Assurance Incentives Driver liaison
Insurance during FOT
Vehicles Drivers Questions / support
Intervention logging
Objective data Subjective data / Documentation
Data pick-up Data pick-up
method method Data storage
method
Data storage Data storage
method method
Data Server FOT Operations
FOT Operations
Data Server Data
Data
18 ITS 2009 Stockholm www.eurofot-ip.eu
19. Data analysis plan
... Questionnaires
VMC-Fleet
CO-, CV-, CV+E-data
VMC level
Data quality
Data quality
analysis
analysis
SQL Database Cost Benefit Analysis
Data processing Quality
Quality
Data processing
assessment
assessment
specific hypotheses
specific hypotheses
Testing of VMC
Testing of VMC
PI‐calculation Global
Global
PI‐calculation
Data quality assessment
assessment
Data quality
analysis
analysis
Data mining
Data mining Testing of common
Testing of common
hypotheses
hypotheses
VMC-Specific Common
Data Analysis
19 ITS 2009 Stockholm www.eurofot-ip.eu
20. Data volumes:
data acquisition & processing
Mass storage
1012
PROJECT
FLEET DATA BASES
(ONE VMC)
109
VEHICLE
Reconstruction
and analysis
Nr. of data
EVENTS
106 EVENT
STATISTICS
PERFORMANCE
103 INDICATORS
ON-BOARD OFF-BOARD
20 ITS 2009 Stockholm www.eurofot-ip.eu
21. euroFOT network
International collaboration
• An effective international cooperation is in
place
• The main objective is to share methodologies
for the design and operation of FOTs
• Focus on:
Design of Experiments
Synchronisation of performance indicators
Common tools for analysis
Comparison of results
• EuroFOT is also contributing to the definition of
a common language
21 ITS 2009 Stockholm www.eurofot-ip.eu
22. Selected lesson learnt / conclusive remarks
• Use specific hypotheses; Prioritize hypotheses; Consider also
combinations of functions
• Define a sound data analysis plan, focused on quality (events,
metrics)
• Include baseline conditions
• Define methods for safety analysis (crashes will be a rare,
hopefully absent event)
• Anticipate how to deal with operational aspects (e.g: maintenance,
assistance to drivers, subjects drop-outs,…)
• Plan pilot tests for a feedback on specifications and a check on
technical / organisational aspects
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