This document discusses tools and processes for analyzing field operational test (FOT) data. It provides an overview of the SAFER analysis platform, which processes complex driving data from multiple sources through several steps: decryption, synchronization, resampling, quality calculation, and event generation. The data is then analyzed to study driver behavior, safety systems, and more. Further development needs include reducing processing time, cloud storage, efficient data structures, automatic video coding, and anonymization solutions while retaining valuable information.
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SC4 Workshop 1: Helena Gellerman: data analyses in transport
1. HOW YOU USE THE
DATA FOR FOT
ANALYSIS
Helena Gellerman, SAFER
2. Content
SAFER overview
Present tools and processes in the FOT
analysis platform
Further development needs for FOT/Pilot data
analysis
3. Swedish Transport Administration
Swedish Transport Agency
Region Västra Götaland
City of Gothenburg
AB Volvo
Autoliv
ÅF
Folksam
If
Lindholmen Science Park
Scandinavian Automotive Suppliers
Scania
Sweco
Volvo Car Corporation
IRezQ
Malmeken
Chalmers University of Technology
University of Gothenburg
Halmstad University
KTH
Lund University
Acreo
SP
Swerea IVF
Swerea SICOMP
TÖI
Viktoria institute
VTI
Borås University
Skövde University
SAFERVehicle and Traffic Safety Centre
30 partners in collaboration
Society
IndustryAcademy &
Institutes
7. Driver and forward camera
Rear cameraEye/head tracker
Logger
Feet camera
Field Operational Test
Equipment
8. Security and analysis platform
Video
Data
Data owners (e.g.
SAFER + OEM)
Request
OK
D e c r y p t d a t a ,
e x t ra c t a ll d a t a
s o u rc e s a n d
s a v e i n
i n t e rm e d ia t e
R a w M a t . m a t
f o r m a t
P e rf o r m p r o c e s s i n g o f
s o m e O E M s e n s it i v e
m e a s u re s t o l e s s s e n s i t iv e
d e ri v e d m e a s u r e s ( p re -p r e -
p r o c e s s i n g o n C A N ) .
I n c l u d e e x t r a c t o f
b a s e l in e / t r e a t e m e n t .
D i s k p lu g g e d i n t o
U S B o n
w o rk s t a t i o n .
M a t la b s c ri p t i n i t i a t e d .
P e r t rip p r o c e s s i n g
s t a rt s . E s t im a t e s p a c e
n e e d e d a n d c h e c k f re e
s p a c e o n t r a n s f e r d i s k s
L o a d v e h ic le
u n iq u e
c o n f ig u r a t io n f i le
a n d c re a t e o u t p u t
d i re c t o r i e s i f
n e c e s s a ry .
T o o s h o r t
t ri p s a r e
d i s c a rd e d
( < X s )
D e c o d e
C A N i n t o
m e a s u r e s
(h u m a n
re a d a b le )
F i x 1 )
t im e s t a m p s
p e r d a t a s o u rc e .
U s e l in e a r
re g re s s i o n t o
a v o i d c l o c k d ri f t
C a l c u la t e
1 )
p r e -
re s a m p le d e r iv e d
m e a s u re s (e . g J e r k ) p e r
d a t a s o u rc e . A d d t o
m e a s u r e s f o r t h is d a t a
s o u r c e i n o D a t a S e t .
I n s t a n t ia t e 1 )
o D B d a t a (r e s u lt
M a t la b f o rm a t )
f o r t h e f i rs t
t i m e .
A d d
m e t a d a t a
1 )
i n f o rm a t io n
t o t h e
o D B d a t a
C r e a t e a c o m m o n
t i m e v e c t o r a t
1 0 H z b a s e d o n
t h e C A N -v e lo c it y
t i m e s t a m p s
R e m o v e t h e f i rs t
5 s 1 )
a n d t h e la s t
5 s 1 )
o f d a t a d u e t o
s t a r tu p / s h u t d o w n
e f f e c t s o n d a t a
s o u rc e s
S a v e t h e f i le i n
t h e in t e r m e d ia t e
fo r m a t
R a w M a t . m a t
C h a n g e m e a s u re
n a m e s a c c o r d i n g
t o c o n f i g u r a t io n
f il e
1 )
(n a m e
h a r m o n iz a ti o n )
A p p ly
1 )
a n t i -
a li a s i n g f il t e r.
A d d m e t a d a t a 1 )
a b o u t f il t e r a n d
re s a m p le d d a t a t o
o D B d a t a
A p p l y re s a m p li n g 1 )
f o r e a c h in d i v id u a l
m e a s u r e s . A d d t o o D B d a t a . m a t (p r e -
p ro c e s s in g re s u l t f o r m a t )
A d d c o m m o n t im e
in f o r m a ti o n f ro m
r e s a m p l in g t o o D B d a t a
(o n e t i m e o n ly )
R u n 1 )
a f i rs t d a ta
v e ri f ic a t io n s c r ip t o n
o D B d a t a . .
C a lc u la t e
1 )
p e r-s a m p le
q u a li t y o n o D B d a t a f o r a
f e w m e a s u r e s / d a t a
s o u rc e s .
C a l c u l a t e
1 )
p e r- m e a s u r e
q u a l i t y o n o D B d a t a f o r
m e a s u re s , u s i n g p e r -
s a m p l e q u a l it y .
C a lc u l a t e 1 )
p e r- t rip
q u a l it y . B a s e d o n p e r
m e a s u re q u a l it y .
C a lc u la t e 1 )
a ll
d e ri v e d
m e a s u r e s .
C a lc u la t e i n t ie rs .
C a lc u la t e
1 )
a l l
e v e n t s (t i e r
b a s e d ) .
C a l c u l a t e 1 )
d e r iv e d
m e a s u re
q u a li t y
C a lc u la t e 1 )
K a l m a n
f i lt e r s e n s o r f u s i o n
f o r p o s i t i o n a n d
h e a d i n g
e n h a n c e m e n t .
H a n d le 1 )
s ig n i f ic a n t d ig i t s
a n d t o o l a rg e /
in f i n it e v a lu e s .
A d d d ri v e r I D
S o rt m e a s u r e s
a n d e v e n t s
a lp h a b e t i c a l ly
( s i m p li f y u s a g e )
S a v e
o D B d a t a o n
s e rv e r a n d
t ra n s fe r d is k .
C h e c k t h a t a l l f i l e s
h a v e b e e n
p ro c e s s e d a n d
r e m o v e d a t a f ro m
o rig in a l d i s k
W h e n t ra n f e r d i s k
is a lm o s t f u ll ,
m o v e t o S A F E R /
C h a lm e r s f o r
u p lo a d i n g
P l u g t ra n s fe r d is k
i n t o U p lo a d i n g
s t a t i o n a t S A F E R
a n d s t a rt D a t a
u p l o a d
C h e c k t h a t a ll f i le s
h a v e b e e n
u p l o a d e d a n d
re m o v e d a ta f r o m
t ra n s f e r d i s k
M o v e t r a n s f e r
d i s k b a c k t o d a ta
p ro v id e rP h y s ic a l m o v e
o f d i s k
1 )
B a s e d o n t h e p e r -O E M i n f o rm a t io n i n t h e M S E x c e l d o c u m e n t c a l le d M E P S . T h i s f i le i s p a rs e d o n M a t l a b a n d a . m a t -f i le c a ll e d o P r e P r o c C f g . m a t
( c o n f ig u r a t io n f i le p e r v e h i c l e ) is u s e d t h o u g h o u t t h e p re - p ro c e s s i n g .
6.Savingand
uploadingof
data
5.Calculate
derived
measures
andevents
4.Calculate
quality
3.Filterand
resampleper
measure
2.Save,fixtime
andhighfreq
derivedmeasures
1.Readand
decypt/decode
data
D is k p l u g g e d i n t o U S B
o n w o r k s t a t io n a n d
f i le s a re c o p i e d
( s e p a ra t p r o c e s s f r o m
t h e o th e r p re -
p ro c e s s i n g . )
I d e n t i f i c a t io n
i f t ri p is
b a s e li n e o r
t re a t m e n t
D o M A P -
m a t c h i n g
(d a t a
e n r ic h m e n t )
a n d a d d t o
R a w M a t
9. Data
Complex data generated and collected from
o In-vehicle network (CAN, LIN, MOST)
o Sensors (accelerometers, head tracker, eye tracker)
o GPS
o Cameras
o Communication data (V2X)
Subjective data (questionairs, manual video
annotations
Contextual data (weather, mapdata)
75000 hours of driving data plus estimated
200000 hours from EU project UDRIVE
10. Data processing steps using
HPC
Decryption
Synchronization
Re-sampling
Harmonization
Creating derived measures
Pre-computed event generation
Data hosted in databases (Oracle/MySQL)
and files for videos
11. Data analysis
Depending on the analysis
Frequently used steps are
o Using pre-computed events or
o Creating new definitions of
events from all datasets
o Sandboxing on a subset of the
database
o Validating data quality manually
o Running algorithm on full
dataset
o Manual coding of events
o Final analysis
12. Research areas
Driver behaviour
Crash causation
Impact of new active safety systems
Intersection safety
Infrastructure design
Mobility
Eco-driving
Development of driver models Automation
13. Further development needs for
FOT/Pilot data analysis
Reduce the time for data transfer/mgmt/processing –
research real-time
Data sent over gprs/wifi to cloud based storage
Principles for edge computing / data abstraction /
aggregation
Efficient data structures – efficient data extraction
Visualisation tools – data mining and analysis results
Automatic video coding – today manual annotations
Open(?) repositories with high quality context information
(maps, weather, traffic conditions)
14. Further development needs
for FOT/Pilot data analysis
Personal integrity
Anonymization without loosing valuable information
Research real time anonymization
Automated vehicles operations and data collection
Solutions:
Data anonymization
Automatic video coding
Open, public data data in secure enclaves
15. Thank you for your attention
Contact info:
Helena Gellerman
Area manager FOT/NDS at SAFER
FOTNet Data –
Data Sharing Framework WP leader
helena.gellerman@chalmers.se
+46 31 7721095
+46 761 191429
Notas do Editor
10 year agreement, 4 M USD
30 million SEK per year (~3 M EUR per year) for 10 years. Founded in 2006.
Data arrives in hard drives – copied and pre-processed (filtered etc)
Currently 9 analysis stations with access controlled rooms.
No internet/mail access or USB extraction
If data to presentation or report =&gt; request to OEM+SAFER =&gt; if ok, extraction