HOW YOU USE THE DATA FOR FOT ANALYSIS

Helena Gellerman, SAFER

Content SAFER overview Present tools and processes in the FOT

analysis platform Further development needs for FOT/Pilot

data analysis

Swedish Transport AdministrationSwedish Transport AgencyRegion Västra GötalandCity of Gothenburg

AB VolvoAutolivÅFFolksamIfLindholmen Science ParkScandinavian Automotive SuppliersScaniaSwecoVolvo Car CorporationIRezQ Malmeken

Chalmers University of TechnologyUniversity of Gothenburg Halmstad UniversityKTHLund UniversityAcreo SPSwerea IVFSwerea SICOMP TÖIViktoria instituteVTIBorås UniversitySkövde University

SAFER Vehicle and Traffic Safety Centre

30 partners in collaborationSociety

IndustryAcademy & Institutes

SAFER research programmes

Crash

Traffic Safety Analysis

Impact

Post-crash

Pre-crash

Each programme governed by reference group

SAFER FOT/NDS activities 2006-2015

Present tools and processes in the FOT analysis platform

Driver and forward camera

Rear camera Eye/head tracker

Logger

Feet camera

Field Operational Test Equipment

Security and analysis platform

Video

Data

Data owners (e.g. SAFER + OEM)

Request

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D e cr yp t d ata ,e x tra ct al l da ta

so urce s a n ds av e in

in te rm ed ia teR a w M at.m a t

fo rm a t

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m e a sure s to les s s e nsiti vede r iv e d m e a sur es (pre -p re -

p ro c e ss ing o n C A N ).I nc lud e e x tra c t o f

ba s e line /tre a te m e nt.

D is k plug ge d in toU S B o n

wo rk s ta tio n .

M a tla b s c rip t in itia te d .P e r trip pro c es s ing

sta rts . E stim a te s pa c en e e de d a nd c he c k f re es pac e o n tra nsf e r d isks

L o a d ve h icleun ique

co nfigura tio n filea nd c re a te o utpu t

dire c to rie s ifn ec e s sa ry .

T o o sh o r ttrips a re

d isca rde d(<X s )

D e co deC A N in tome a s u re s( hum a n

re a da b le )

F ix 1) tim es ta m pspe r da ta s ou rc e.

U se l ine a rreg re ss ion to

a v o id c lo c k dr ift

C a lc u la te 1) pre -re sa m ple de rive d

m e a s ur e s ( e.g J e rk ) pe rda ta so urce . A dd to

m ea s u re s fo r th is da taso urc e in o D ata S e t.

Insta ntia te 1)

o D Bd ata (r e su ltM a tla b fo rm a t)

f or th e fir stt im e .

A ddme ta da ta 1 )

in fo r m atio nto the

o D Bda ta

C re a te a c o mm o ntim e ve cto r a t

10 H z ba se d o nthe C A N - ve lo city

tim e s ta m ps

R e m ov e the fir st5s 1) a nd the la st

5s 1) o f da ta due t os ta rtup /s h utdo w n

e ffe cts o n da taso ur ce s

Sa v e the file inthe i n te rm e d iate

fo rm a tR a w M a t.m a t

C ha nge m e a sur ena m e s a cc o rd ingto co n figura tio n

file 1 ) (na m eha rm o n iz a tio n)

A pp ly1 ) an ti-a l ia s in g fi lte r .

A dd m e ta da ta 1)

a bo u t fi lte r a n dr e sa m ple d d a ta to

o D B da ta

Ap ply re sa mpl ing 1 ) fo r e a ch ind iv idua lm e a s ur e s . A d d to o D B da ta .ma t ( p re -

pr oc e s sing re s ult fo rm a t)

A dd co m m on tim ein fo rm a tio n fro m

re s a m pl ing to oD B da ta(o ne time o n ly)

R un 1 ) a fir st da tav e r ifica tio n sc ript o n

o D B da ta . .

C a lcu la te 1) pe r- sa m plequa l ity o n oD B da ta fo r a

fe w m e a s ure s /da tas o ur ce s .

C a lc u la te 1 ) pe r-m e a s ureq ua lity o n o D Bda ta fo rm e a su re s, us ing pe r-

sa m ple q ua l ity .

C alc u la te 1 ) pe r -tripqua lity. B a se d on pe r

m e a sur e qua lity.

C a lcu la te 1) a llde r iv e d

m ea s ure s .C a lcu lat e in tier s .

C a lc u la te 1) a l lev e nts ( tie r

ba s ed) .

C a lc u la te 1 )

de rive dm ea s u requa l ity

C a l cu la te 1) K al m a nfi l te r se n s o r fus io n

fo r po s itio n a ndhe a d ing

e nha n ce m e n t.

H and l e 1)

sign ific ant d ig itsa n d to o la rge/in fin ite va lue s.

A dd drive r ID

S o r t m e a s ure sa nd e ve nts

a lpha b e tica l ly(sim p li fy usa g e )

S a v eoD Bda ta o nse rv e r a nd

tra ns fe r d is k .

C he ck tha t a ll file sha v e be e n

pro ce s se d a ndre m o ve d ata f ro m

o rig in al d is k

W h en tr a n fe r d iski s a lm o st fu l l,

m o v e to S A F E R /C halm e rs fo r

up lo a d ing

P lug tr ans fe r d iskin to U p lo a d ing

s ta tio n a t S A F E Ra nd s ta r t D a ta

up lo a d

C he ck that al l fi le sha ve be e n

up lo a de d a ndre m o ve da ta fro m

tra ns fe r d isk

Mo v e tra ns fe rd is k ba c k to da ta

pro vide rP hys ica l m ov eo f d is k

1) B a se d o n the pe r-O E M in fo rm a tio n in the M S E xc e l do c u m ent c a lle d M E P S . T h is fi le is pa rs ed o n M a tla b an d a .m a t-f ile ca l le d o P re P ro c C fg .m a t(c on fig ura tio n file pe r ve hic le ) is us e d tho ugho u t the pr e -pro c e ss ing .

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fi le s a re c o p ied(s ep a ra t pro ce ss fr o m

the o the r p re -p ro c e ss in g .)

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Data Complex data generated and collected from

o In-vehicle network (CAN, LIN, MOST)o Sensors (accelerometers, head tracker, eye tracker)o GPSo Cameraso 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

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

Data analysis Depending on the analysis Frequently used steps are

o Using pre-computed events oro Creating new definitions of

events from all datasetso Sandboxing on a subset of the

databaseo Validating data quality manuallyo Running algorithm on full

dataseto Manual coding of eventso Final analysis

Research areas Driver behaviour Crash causation Impact of new active safety systems Intersection safety Infrastructure design Mobility Eco-driving Development of driver models

Automation

Further development needs for FOT/Pilot data analysis

Reduce the time for data transfer/mgmt/processing – research real-timeData sent over gprs/wifi to cloud based storagePrinciples for edge computing / data abstraction / aggregationEfficient data structures – efficient data extractionVisualisation tools – data mining and analysis resultsAutomatic video coding – today manual annotationsOpen(?) repositories with high quality context information (maps, weather, traffic conditions)

Further development needs for FOT/Pilot data analysis

Personal integrityAnonymization without loosing valuable informationResearch real time anonymizationAutomated vehicles operations and data collection Solutions:Data anonymizationAutomatic video codingOpen, public data data in secure enclaves

Thank you for your attention

Contact info:Helena Gellerman

Area manager FOT/NDS at SAFERFOTNet Data –

Data Sharing Framework WP leaderhelena.gellerman@chalmers.se

+46 31 7721095+46 761 191429