sip-adus workshop 2018...#17820 · 17zl00xx.pptx slide no. 3 2017/11/16 © fka 2018 · all rights...

© fka 2018 · All rights reserved2017/11/16Slide No. 1#17820 · 17zl00xx.pptx

Tokyo, 14th November 2018

Dr.-Ing. Adrian Zlocki, Christian Rösener, M.Sc., Univ.-Prof. Dr.-Ing. Lutz Eckstein

A Traffic-based Method for Safety Impact Assessment of Road Vehicle Automation

SIP-adus Workshop 2018

Forschungsgesellschaft Kraftfahrwesen mbH Aachen


MotivationPublic View on Automated Driving

► Research QuestionWhat is the safety level of automated driving?

► MethodologyA Traffic-based Method for Safety Impact Assessment of Road Vehicle Automation

How SAFE is Automated Driving?


I M P A C T A S S E S S M E N T

S A F E T Y A S S U R A N C E

Effectiveness

Controllability &Functional Safety

„Usability“

Pote

ntia

l con

flict

Acceptance Safety ofInteraction

Efficiency

Evaluation MethodologyImpact Assessment vs. Safety Assurance

SOTIFOperational Safety

Behavioral Safety


Analysis of Automated Driving Field Test DataScenario Classification of Real-World Data

FOTs

Par

amet

er x

Parameter z

Par

amet

er x

Parameter y

New generated situationsVariation of measured situations

Accident dataGeneration of new situations+

Variation of measured situations

Source: Eckstein, L., Zlocki, A.: Safety Potential of ADAS - Combined Methods for an Effective Evaluation, 23rd ESV 2013, Seoul, 2013


Impact Assessment of Automated DrivingDriving Scenarios from Accident Type

Overtaking oncarriageway

646 - Overtaking

631 – DeceleratingCut-In (right)

643 – Decelerating Cut-In (left)

Example: Passive Cut-In


Impact Assessment of Automated DrivingDriving Scenarios from Accident Type

Approach: The types of driving scenarios, respectively physicalaccident constellations, do not change with automated driving.

The frequency of occurrence and the severity of these drivingscenarios may change with automated driving.


Impact Assessment of Automated DrivingDefinition of Methodology for Impact Assessment

∆f(Sn)

AD

Ref

para

met

er i

parameter n

para

met

er i

parameter n

S1

S2

Sn

∆ Frequency of Occurrence

Scenarios

Derivation of Effectiveness Field

Automated driving function

∆ Severityin

Scenarios

Impact of automated driving function

Definition of Scenarios

∆ Frequency of occurence ofdriving scenarios

∆ Severity indriving scenario

Driving scenario-basedestimation of effectiveness field

1

2

3

4 5

6

3

45


Impact Assessment of Automated DrivingDefinition of automated driving functionand scenarios

200

m80

m 20°

60°

10 m

10 m

20°

20°

200

m

Fahr

trich

tung

Motorway-Chauffeur

Operational design domain (ODD):

Relevant driving scenarios:Driving

accidentApproachingstatic Object

Approachingvehicle

Approachinglateral Object

Automation level (SAE): 3

Traffic Jam

Cut-inLane change Take over

Turn at intersection

Crossingintersection Turn around

Operation domain:

12

21


v

Impact Assessment of Automated DrivingEffectiveness Field and Scenario Classification

FOTs

Par

amet

er x

Parameter z

Accident data

Scenario „Approaching vehicle“

Scenario „Lane change“Scenario

„Cut-in of other vehicle“

3


Driving scenario-based estimation of effective. fieldAccidents in Germany according to ODD

Federal motorway

6%

Federal highway

17%County road

14%

Rural road25%

Municipal road38%

Accidents in Germany in 2016308.145 A(P)

Accidents in domain „Motorway“19.010 A(P)

no car participation

11%

functional limits 17%

driver and vehicle related limits

11%

not adressable driving scenarios

14%

Effectiveness field 47%

3


Driving scenario-based estimation of effective. fieldInput data for scaling-up and simulation

Approaching traffic jam

18 %

Driving accident

17 %

Approaching leading vehicle

43 %

Passive cut-in16 %

Driving scenarios in effectiveness field

9.395 A(P)

Approaching static object2 %

Lane change4 %

Number of accidents forscaling-up of effectiveness

Situational variables foreffect of function by

simulation

Accidentstatistics

GIDAS

FOT

Driving scenario Accidents

Passive cut-in 1.219

v1=67 km/h

v2= 93 km/h

3


Safety Impact Assessment of Automated DrivingIdentification of ∆ frequencies of driving scenarios

∆f(Sn)

AD

Ref

∆ Frequencies of driving scenariosDriving scenario

based estimation ofeffectiveness field

Automated drivingfunction

∆Severityin drivingscenario

Impact of automateddriving function

Definition of drivingscenarios

∆Frequencyof drivingscenario

4


Identification of ∆ frequencies of driving scenariosFOT-data

Ref AD

Driving scenarioclassification

∆frequencies ofscenarios

© ika

AD

4


Impact Assessment of Automated Driving Identification of ∆ Frequency from FOT Data

AD

4


Identification of ∆ frequencies of driving scenariosTraffic simulation4


∆ Severity in driving scenarios by re-simulationSimulation framework

Simulation of reference

∆ Severity

Driving scenario„passive cut-in“

Ref

Simulationwith ADF

AD

► Human driverperformance modelsfrom driving simulator

study/FOT for reference

5


1% 3%5% 8% 4%

17%

30%

53%

4%

13%

24%

46%

4%

14%

26%

50%

3%

14%

26%

54%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

5% 25%

50%

100% 5% 25

%

50%

100% 5% 25

%

50%

100% 5% 25

%

50%

100% 5% 25

%

50%

100%

Safety Impact Assessment of Automated DrivingImpact Assessment Results 6

Traffic Jam-Chauffeur

Motorway-Chauffeur

CommuterChauffeur

Universal-Chauffeur

Urban Robot-Taxi

Effe

ctiv

enes

s in

dom

ain


Safety Impact Assessment of Automated DrivingImpact Assessment Results 6

0

50,000

100,000

150,000

200,000

250,000

300,000

5% 25%

50%

100% 5% 25

%

50%

100% 5% 25

%

50%

100% 5% 25

%

50%

100% 5% 25

%

50%

100%

Acc

iden

tsw

ithpe

rson

al in

jurie

spe

r yea

r

accidents in domain, of themwithout involvement of passenger caroutside the functional limitsnone or ambiguous effectavoided


Motorway-Chauffeur

CommuterChauffeur

Universal-Chauffeur

Urban Robot-Taxi


Safety Impact Assessment of Automated DrivingImpact Assessment Results

1% 3% 5% 8% 4% 17% 30% 53% 4% 13%24%

46%

4%

14%

26%

50%

3%

14%

26%

54%

0

50.000

100.000

150.000

200.000

250.000

300.000

5% 25%

50%

100% 5% 25

%50

%

100% 5% 25

%

50%

100% 5% 25

%

50%

100% 5% 25

%50

%10

0%

Acc

iden

ts w

ith p

erso

nal

inju

ries

per

yea

r

accidents in domain, of themwithout involvement of passenger caroutside the functional limitsnone or ambiguous effectavoided

effectiveness in domainX %

Motorway-Chauffeur


Commuter-Chauffeur

Universal-Chauffeur

Urban Robot-Taxi

6


Safety Impact Assessment of Automated DrivingKey results

Motorway-Chauffeur can reduce 30 % of all accidents on German motorways at a market penetration of 50 %. This equals 2 % of all accidents on German roads.

The Urban Robot-Taxi can avoid 26 % of all accidents with personal injury within city-limits at a market penetration of 50 %. This equals 17 % of all accidents on German roads.

However, there will be accidents remaining that automated vehicles cannot avoid (due to weather conditions or physics). But we can show that a human cannot avoid these accidents either.

AD Ref

6


Piloting Automated Driving on European RoadsL3Pilot – Real World Data for Impact Assessment

Large-scale Level 3 piloting 1,000 test drivers,100 vehicles

in 11 European countries EC funded in Horizon 2020 34 partner Budget: 68 € Mio., Funding: 36 € Mio.

Website: http://www.l3pilot.eu


L3Pilot Evaluation Levels

Data Management

Technical & Traffic Evaluation

Socio-economic impact evaluation

User evaluation

Impact EvaluationSafety impact (e.g. avoided accidents)Environmental impact (e.g. fuel consumption)on European target level

Objective driving data

Europe

Subjective data

∆ Frequency of driving scenariosDMs, PIs per driving scenarios

e.g. Effect in Transition of controlLong-term effects, usage


L3PilotEvaluation Workflow

23

Technical & Traffic Evaluation

Vehicles – Obj. Data Subj. Data

Digital versionCDF Time series data

User & AcceptanceEvaluation

Impact Assessment

Estimated Impacts

Socio-Economic Impact Assessment

Impact Assessment

Socio-Economic Impact

Technical Evaluation

User Evaluation

Other inputs(e.g. accident & baseline data)

Performance Indicators per driving scenario


Summary

Prospective safety impact assessment for automated driving requires new methodologies

Automated driving provides many challenges with regards to impact assessment since limited real world data is available yet and many new aspects (e.g. user-interaction) needs to be taken into account

Safety impact assessment shows positive results with different automation function

Current research in L3Pilot start data collection for safety impact assessment

Safety Impact Assessment in L3Pilot will provide results based on data from vehicles combined with simulation for the first time


PhoneFax

EmailInternet www.fka.de

fka Forschungsgesellschaft Kraftfahrwesen mbH AachenSteinbachstr. 752074 AachenGermany

Contact

Dr.-Ing. Adrian Zlocki

+49 241 80 25616+49 241 8861 110

[email protected]

THANK YOU FOR YOUR ATTENTION!

QUESTIONS?

sip-adus workshop 2018...#17820 · 17zl00xx.pptx slide no. 3 2017/11/16 © fka 2018 · all rights...

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