This document provides a summary of some preliminary project results from Trent Victor at SAFER and Volvo Technology. It discusses naturalistic driving studies and field operational tests, which combine unobtrusive data collection from drivers' own vehicles with quasi-experimental methods. The document also summarizes the goals, methodology, and current status of the SeMiFOT naturalistic field operational test being conducted by SAFER, which involves collecting data from 18 vehicles in Sweden and 2 in the US to evaluate active safety systems and their effects on driver behavior, crash risk, and acceptance.

1. - some preliminary project results
Trent Victor
SAFER and Volvo Technology
2009-01-08

2. Background

3. Background Motivations
• What causes accidents?
– Greatly increased knowledge of driver behavior, ”the Human Factor”, as
a contributor to crashes.
– Study how driver interacts with vehicle, safety systems, road, traffic,
weather, etc
• What can we do about them?
– Evaluation of new technology (e.g. active safety systems)
– Development of new technology and countermeasures based on the
findings
– How to improve crash-avoidance behaviors
“Naturalistic driving studies are defined as those undertaken using unobtrusive observation
or with observation taking place in a natural setting” (Dingus, et al. 2006).
Field Operational Tests are defined as “a study undertaken to evaluate a function, or
functions, under normal operating conditions in environments typically encountered by the
host vehicle(s) using quasi-experimental methods” (FESTA, 2008)
Naturalistic Field Operational Tests combine both – this is the focus of SAFERs partners

4. Naturalistic driving (ND) data collection
- Natural driving, no special instructions, own vehicles, no
experimenter present, unobtrusive data collection
instrumentation…
is used to
Driver Factors Crash Risk
- Permanent: Age, Experience, Style… - Relative risk, Population attributable
- Transient: Drunk, Tired, Distracted… risk…
assess the
DVE Factors
Vehicle Factors relationship Driver Behavior
- Permanent: Vehicle type, Spec… between - Control behavior (lat, long), Attention,
-Transient: Active Safety, Nomadic Dev… Decisions, Usage/adoption, Event
involvement…
Environment Factors
Countermeasure effectiveness
- Permanent: Speeds, Road type…
- Active Safety, Road treatments, etc
- Transient: Weather, Lighting…
“Naturalistic driving (ND) data collection is used to assess the relationship of (permanent
and transient) Driver-Vehicle-Environment (DVE) factors with crash risk, driving
behavior, and countermeasure effectiveness.” Naturalistic Field Operational Tests

5. UMTRI: FCW
UMTRI: LDW
Japan1: Crossing Road
Japan2: Frontal Collision
Japan3: Drowsiness
Japan4: Near miss
VTTI: 100-Car study
SAFER: TSS FOT

6. Lane position
Lane exceedence
20
Steering angle Eyes on road
Eyes off road
0
Lamp pole
Inform Warn Warn Act
here? here? here? here?
-20
t

7. Collision
Front
O
Directly Safety Related:
Crash
Near Crash
Time to Longitudinal Collision
Incident
Indirectly Safety Related:
Events of Interest
Infinity
Undisturbed Passages
X
Crash avoidance
Collision
Back
behaviors
O
O Infinity O
Collision Left Time to Lateral Collision Collision Right

8. Event of relevance for research
(e.g. Accidentology)
Event of relevance for evaluation of
Safey System X Fatal Crashes as defined in databases
Injury (police/ambulance-reported)
(light/moderate/severe)
Crashes as defined in the 15
100-Car study Police-Reported
Property Damage Only
Non-police-reported
Property Damage Only 5 x police-reported (PR)
Crashes
Non-police-reported
Physical contact or tire strike 67
50 x PR crashes
X Near crash 761
stem
FOT/NDS 550 x PR
ty sy
Incidents crashes
8295
Safe
Events of interest
Exposure/occurancy

9. SHRP2 (USA)
• Extensive observations of driving • Representative sample of crash
behavior data and near-crash data
• >2500 cars for 2 yrs • Databases available for “the next
generation of traffic safety
• Active Safety subset of 500 cars…
researchers”
2007 2008 2009 2010 2011 2012
Track 1: In-Vehicle Study
Study S06: Technical Coordination and Independent Quality Assurance for
Design & S05: Design of the In-Vehicle Driving Behavior Field Study--$3M
Field Data and Crash Risk Study--$3M
Collection S07: In-Vehicle Driving Behavior Field Study--$28M
S03: Roadway
Roadway
Measurement System S04: Acquisition of Roadway Information--$3.5M
Data
Evaluation $0.5M
S01: Development of Analysis Methods Using S08: Analysis of In-Vehicle Field Study Data and
Analysis Recent Data--$1.5M (multiple Countermeasure Implications--$4M
awards, two phases) (multiple awards, different letting schedules)
S02: Integrate Methods and
Develop Analysis Plan--$0.5M
S11: Analysis of Site-Based Field Study Data and
Countermeasure Implications--$2M
Track 2: Site-Based Study (contingent project)
Study
S10: Design and Conduct of the Site-Based
Design & S09: Site-Based Video System Design
Field Study--$11M
Field Data and Development--$1M
(contingent project)
Collection
Revised December 2007

10. Consumer Systems
Risk management systems Japanese systems
(for e.g. fleets, parents) (insurance-driven - taxi, fleet)
Pay-as-you-drive
Remote diagnostics (insurance-driven)
and fleet management

11. Field Operational Test (FOT) start-up at SAFER
Europe FESTA EuroFOT (150*) FOTNet INTEND
Methodology Impacts Coordination Methodology
Establish BASFOT
SAFER FOT Competence build-up
TSSFOT (2*)
Sweden Methodology
SeMiFOT (18*)
Methodology
USA UMTRI UMTRI SHRP2
2006 (3MSEK) 2007 2008 (100MSEK)
Competence Project Proposal * Swedish vehicles

13. • Part of Sweden-Michigan partnership agreement
• Main Goals:
– to further develop the Naturalistic FOT method into a powerful
tool for
a) Accidentology
b) Evaluation of safety, efficiency, and usage &
acceptance
c) Countermeasure innovation and development
• 18 vehicles in Sweden and 2 vehicles in USA, ca 6 months
data collection, duration Jan 2008-June 2009

14. WP2 – Methodology and FOT Design

15. WP2 – Methodology and FOT Design
• Identification of function and vehicles
– The selection of systems is more guided by what systems are available and what systems
the manufacturers wanted to include in this project
– On-market vehicle-integrated systems and one after market system
• Definition of objectives, hypotheses, and performance indicators for each function
– Next slides
• Specification of experimental procedures
– FESTA Handbook
– Study plan was submitted for ethical review – Data and personal integrity, data ownership
and sharing, much more complicated and multi-faceted than assumed. Many legal issues,
e.g. responsible for filming.
Decision from ethical committee in Gothenburg – this study does not need ethical
approval.
– Flexibility in experimental procedures is rather constrained by practical issues, OEM, and
safety requirements
– Vehicles and drivers selected from manufacturers or manufacturer-associated companies –
Primary car drivers (and family members) vs truck drivers
– Comparable scenarios in the baseline data, when the function is turned off, and in the
treatment data, when the function is turned on. Changes over time.
– AB design, no baseline for some functions (e.g. ESC)
– a relatively large number of questionnaires

16. y
l og
nto
i de S
A cc A CC LD
W
B LI FCW E SC IW
CR-Events-Prevented Analysis
Safety
-””What-if” no system acted?” analysis
Crash-Relevent Event Analysis 25 45 5 50 65 10 315
-Multiple regression etc, relating 115 (40%)
Precursor, Outcome, Mediating factors
Acceptance Usage Attention
Visual Behavior Analysis
-Glance behavior ”function”, Distraction 45 75 35 30 75 0 30 290
events (37%)
Usage Analysis
-Quantify usage in select situations 19 59 4 4 19 4 109
(14%)
Acceptance Analysis
-Quantify acceptance, relate to usage 11 41 6 6 2 0 66
(8%)
160 130 180 45 135 86 44 780
(21%) (17%) (23%) (6%) (17%) (11%) (6%) (100%)

17. Hypothesis example (ACC)

18. Conclusions on Hypothesis Prioritizations
• Safety and Attention analyses should be prioritized as they received
77% of the prioritization points, whereas the Usage and Acceptance
analyses received 22%.
• LDW, Accidentology, ACC, and FCW should be the prioritized
applications of the analysis.
• Further prioritizations:
– Within Safety analyses, prioritize analysis of crash-relevant events (i.e.
kinematic- and system triggers)
– Within Attention analyses, prioritize analysis of eyetracker data in selected
situations
– Within Usage analyses, prioritize analysis of usage for the LDW, ACC, and ESC
functions.
– Within Acceptance analyses, prioritize analysis of acceptance questionnaires.

19. WP3 – Data Management

20. Virginia Tech Naturalistic Driving Equipment (SHRPII study)
2600 vehicles!!
Presentation by Tom Dingus as SHRP 2 Safety Research Symposium, July 17-18,2008, Washington, DC

21. Virginia Tech Naturalistic FOT Equipment (100-car study)

22. UMTRI Naturalistic FOT Equipment

23. SAFER Data Acquisition System
Extra ”external” sensors
Accelerometers
Eyetrackers – SeeingMachines/SmartEye (13units total)
Lanetracker/ForwardDistVel – MobilEye (15 units)
Tw
GPS (1 Hz) dat o diffe
sys a acq rent
eva tePC ustio
m
lua s n
CAN ted
Steering Wheel Angle
Turn Indicator
Gear Level Position
Accelerations
Etc …..
Hard
Video (Analogue) drives
6 Cameras in total

24. Data Collection and Storage
Technologies

25. Database and Storage
• Very large data volumes!
• SeMiFOT:
Data
– Video: 8 Terabyte
– Data: 1 Terabyte
Video
• euroFOT (Sweden only):
– Video: 50-100 Terabyte
– Data: 6 Terabyte

26. Analysis tools
– Direct database use/searching Data
– Event identification
– Synchronized data with video Video
– Easy manual and automatic
annotations
[Show video]

27. WP4 – Vehicle and Test Management

28. Current status:
• 3 VTEC – 2 trucks running
• 3 SAAB – 2 cars running
• 4 SCANIA – currently installing
• 8 VCC-6-7 cars running with TSS-FOT
logger
Some aspects:
• Installation and verification
• Pickup of data in vehicles
• On-line quality control
• Hotline and support organization
• Data uploading

29. WP5 – Evaluation of Methodology

30. WP5 – Evaluation of Methodology
• Consultations with UMTRI, SHRP2, Guest
researcher visit from IOWA (SHRP2 Analysis),
FESTA, FOT-NET, EuroFOT
• Daunting, complex task but there are some true
opportunities, e.g. eyetracker data, events-
prevented analyses, etc.

31. Collaboration with SHRP2
SHRP2 is within the Transportation Research Board (TRB) of the
National Academy of Sciences (NAS)
1. SeMiFOT as a collaboration probect with SHRP2 – Loan staff visit
to SHRP2, Technical Expert Group participation
2. Memorandum of Understanding regarding information
exchange between NAS (TRB) and Sweden (SRA and VINNOVA
for SAFER)
Return visit by SHRP2 to Sweden in Feb/March

32. Conclusions
• Ongoing project, new methods and technology
are being developed for the first time in Europe.
Has more of a methods development character.
• Has given Sweden and SAFER partners a
leading position in EU and internationally
• Good collaboration with Michigan (UMTRI)
• Complex project in many regards

33. Borderless research to save lives
www.chalmers.se/safer
safer@chalmers.se

34. Method Chain in Relation to NDS & FOT
Experimental Collection Analysis
Design Phase Phase Phase
NDS
Low
Naturalistic
Driving Studies
Different Analysis Goals
Naturalistic
(NDS)
Driving Studies
Level of experimental control
(NDS)
FOTs
Naturalistic FOT
Naturalistic
Tools
FOT
Other FOTs
Other
e.g. test routes
FOTs
High

35. Naturalistic Methodology
in Relation to Existing Methods
Aggregated data of Pre-Crash behaviour, initiated by Crash
Events (e.g. questionnaires)
In-depth studies of Pre-Crash behaviour, initiated by Crash
Events (e.g. on-site investigations and interviews) Enabled by new
data collection
technology
Naturalistic Methodology – Objective longitudinal data (high km),
large number of cases, unobtrusive instrumentation, no
experimenter present, driving their own vehicles,
tens to thousands of vehicles, etc
Experimental Field Studies – low km, short time-scale,
Experimental control, specific routes, few cases, ca. (1-10 cars)
etc
Experimental Lab-, Simulator-, and Test Track Studies

36. Factors Influencing Choice of Objectives
1. Opportunities
• Study new issues, develop innovative methods,
2. Resources
• Time (hrs and calendar)
• People with the right competence
3. Diverging partner interests
• Especially OEM constraints (e.g. y-data)
4. Data reduction limitations
• Ease of implementation limited by technology,
difficulty of Performance Indicator calculation etc,
manual data-reduction