APPLICATIONS IN TRAFFIC ACCIDENT RESEARCH TO IMPROVE … · Verkehrsunfallforschung ein Unternehmen...

Verkehrsunfallforschung

ein Unternehmen der

an der TU Dresden GmbH

APPLICATIONS IN TRAFFIC ACCIDENT

RESEARCH TO IMPROVE VEHICLE SAFETY

MATLAB EXPO 2018, Germany

Dipl.-Ing. Florian SpitzhüttlInstitute for Traffic Accident Research

at Dresden University of Technology

2

1. Necessity of traffic accident research

2. Application assisted accident investigation

3. Data analyses for research on traffic safety

4. Pre-crash simulation to enhance traffic safety

5. Conclusion

Structure

3

Accident research in the 1920s

Applications in Traffic accident research to improve vehicle safety

Necessity of traffic accident research

Source: YoutubeEarly “accident research” in Dresden

4

Accident scenario in Germany

o Car occupants benefit from

active and passive safety

o Numbers of accidents &

casualties are stagnating

since some years

o In 2016 persons:

• Fatalities 3,206

• Seriously injured 67,426

• Slightly injured 329,240



20%

40%

60%

80%

100%

120%

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

All accidents

Accidents w/ personal damage

Fatalities

Seriously injured persons

source: DESTATIS Fachserie 8 / Reihe 7

- 9%

- 12%

- 40%

+

15%

In-depth accident studies are absolutely essential to improve vehicle safety

5



Documentation of

real traffic

accidents

Accident data

analysis

Naturalistic

driving study

Area of studies

• Automotive engineering

• Transportation engineering

• MedicineEducation

System

assessmentInstitute for Traffic Accident Research

at Dresden University of Technology

Reconstruction

and Simulation

6



General information

Accident sketch

Technical investigation

Medical investigation≈ 2.000

accidents/year

GIDAS – German In-Depth Accident Study, since 1999

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Source: Google Maps & GIDAS

Accident level

Vehicle level

Personal level

…

~33.000accidents

~60.000vehicles

~77.000persons

Ø 3.500 single

information/accident

Only accidents with

personal damage

Criteria Investigation area Database

Hanover

Dresden

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5. Conclusion

Structure

9

Some examples


Application assisted accident investigation

OpenStreetMap (OSM)

for accident sketch

Coding of injuries Signal processing

of measurements

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5. Conclusion

Structure

11


Data analyses for research on traffic safety

Databases Access and processing

Source: ESV 2017 – Bakker, Spitzhüttl et al.:

“IGLAD - International harmonized in-depth accident data”

12

Mathematical models – Injury Risk Functions (IRF)

What is it?

Model to describe the probability of the occurrence of a specific event (e.g. to be at least

seriously injured) as a function of one or several influencing parameters (e.g. collision speed)

for a given population.

Substantial tool for the assessment of vehicle safety systems

How is it calculated?

Based on real (accident) data, calculating the maximum likelihood estimation with an

underlying logistic distribution



0 ... n – regression coefficients

x1 ... xn – independent variables

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What is it used for?



p(slightly injured) = 17%

p(seriously injured) = 60%

p(slightly injured) = 26%

p(fatal) = 12%

p(seriously injured) = 62%

p(fatal) = 23%

Real accident:

vcoll = 50 km/h

Accident with system

(e.g. AEB):

vcoll = 40 km/h

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Multidimensional



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Calculation of deformation frequencies

o Normalized car dimensions

and discretization into voxel

o Accumulation of accident deformations

for 1000 passenger car

Analyzation of potentially safe

places for sensitive and/or

dangerous energy storage

(e.g. battery or gas)



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5. Conclusion

Structure

17

ACEA Safety Model


Pre-crash simulation to enhance traffic safety

Phase 4

In-Crash-Phase

Phase 1

Normal driving

Phase 5

Post-Crash-Phase

𝑡𝑢collision unavoidable

Phase 3

Pre-Crash-Phase

Sequence of a traffic accident

𝑡0collision

𝑡𝑐𝑟𝑖𝑡critical event

Phase 2

Incident-Phase

Tertiary safetyActive safety Integral safety Passive safety

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VUFO Accident Simulation Toolbox (VAST)

Preparation

action

roadvehicle

Sketch

External data

Virtual camera images

sent back to Simulink.

Source: Mathworks.com

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Example accident – Sketch Accident scene

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Example accident – Simulation



real accident situation real accident situation

with ADAS System

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Evaluation of opponent‘s position at specific TTC

* TTC = 400 ms

· TTC = 200 ms

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Point of no return tu when a collision is unavoidable



Source: Winner et al., H. (2015).

„Handbuch Fahrerassistenzsysteme,

Grundlagen, Komponenten und Systeme

für aktive Sicherheit und Komfort“

(4) - Max. deceleration + Steering to the left

(5) - Max. deceleration + Steering to the right

(6) - Max. acceleration + Steering to the left

(7) - Max. acceleration + Steering to the right

(1) - Max. deceleration

(2) - Steering to the left

(3) - Steering to the right

(8) - Max. acceleration

Circle of forces / „Kamm‘scher Kreis“

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Point of no return tu when a collision is unavoidable

o Criticality as a function of time

• continuous

• differentiable

o No knowledge about the exact function

o f(tu) = 0

• no analytical solution possible

• approximation by iterative process

and variable integration step size

Efficient 2-step-approximation method

1) Fixed step size of 1s

2) Bisection method



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Point of no return tu when a collision is unavoidable – Generic rear-end collision



tu = f(∆v, μ); vobj = 40 km/h

∆v = 2 …100 km/h , μ = 0,1... 1,0 tu comparison of simulation and literature

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Naturalistic driving study (NDS) Incidents and Events



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Naturalistic driving study (NDS)

Real scenario Recording



o Camera

o Accelerometer

o Rotation rate sensor

o GPS

o Sender and receiver device

o Processor und ring memory

Position Movement Video

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Real scenario Simulation



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Ground truth labeling with



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0 50 100 150 200 250-1

0

1

2Anfahrverhalten

Zeit in s

Längsbeschle

unig

ung m

/s²

0 50 100 150 200 2500

20

40

60

GP

S G

eschw

indig

keit k

m/h

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Source: GDV, VUFO (2016): „Unfälle mit Beteiligung von Wohnmobilen in Deutschland“

Driver 1 Driver 2 Driver 3

Pas

sern

ger

car

Cam

pin

g v

an

Driver 1:o Large scatter range

o Significant difference between

passenger car and camping van

o High accelerations

Driver 2:o Marginal difference between

passenger car and camping van

o Experienced driving

Driver 3:o Low scatter range

o Higher acceleration in passenger car

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VW Caddy:

Responsibility for children

Renault Espace:

Marital-problems

Suzuki Swift:

Divorce

Renault Megane:

New relationship

Renault Megane:

Crisis in relationship

Renault Laguna:

On-Off relationship20

17

20

15

20

16

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Real driving behavior Driver models Automated driving

Individual sets of

driving parameters

Extraction of driving

profiles

Train ADAS

and HAD

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5. Conclusion

AGENDA

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o Assurance of traffic safety must be a very high society target.

Human errors must not lead to fatalities in a modern traffic environment!

o In contrast to past trends, recent statistics show a

stagnation in the accident numbers.

o The development of Highly Automated Driving needs some more

efforts to ensure a safe and modern concept of movement.

o Therefore it is very important to improve on crucial aspects of

• ensuring functional safety

• study real world scenarios

• progress on perception infrastructure to support vehicle systems.


Conclusion

Verkehrsunfallforschung

ein Unternehmen der

an der TU Dresden GmbH

THANK YOU

FOR YOUR ATTENTION!

Florian Spitzhüttl

Data analyses and simulation

[email protected]

Tel.: +49 351 43 89 89 22

mailto:[email protected]

APPLICATIONS IN TRAFFIC ACCIDENT RESEARCH TO IMPROVE … · Verkehrsunfallforschung ein Unternehmen...

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