vortrag1400 andreaskuhn ki-bigdataanalytics mdz11-v01
TRANSCRIPT
A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Dr. Andreas KuhnA N D A T A
Artificial Intelligence and Big Data Analyticsin Mobility of the Future
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
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Fields of Competence• Artificial Intelligence• Data Mining• Big Data Analytics• Modeling and simulation• Predictive Model based
Control• Distributed Control• Signal Classification• Swarm Intelligence• (Embedded) Software• Decision Support Systems• Robustness and Complexity
Management
Contact: A-5400 Hallein, Hallburgstraße 5, +43 6245 74063, [email protected], www.andata.atA-1050 Vienna, Straußengasse 16
Data drivendevelopment
process
Automotive safety
(Mobile) RoboticsAutomated Guided
VehicleIndustry 4.0,Digitalization
Failure prediction
Big Data Analytics &AI & Simulation
Anomalies andIncident Detection
Vehicle and trafficautomation
since 2004
A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Advanced Driver Assistant Systems and Automated Driving
Ø Which sensors are necessary for valid decisions in automated driving?Ø What senseful functions can be carried out with a given set of sensors?© 2018, ANDATA, several granted and pending patents
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Avoiding collisions by informing, warning,braking, steering, automated manoeuvres
© A
udi A
G
A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Artificial Intelligence
Criteria for Intelligence (according Russel, Norvig: Artificial Intelligence, A Modern Approach, Chapter 2)
• Detection and interpretation of the situation• Judgement and pondering upon different action alternatives• Adaption to changing environments and new situations• Goal-oriented procedures
Ø „Intelligent Systems“:ALL those criteria are in place in common
© 2018, ANDATA
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A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Example Detection of Objects with Deep Learning
ü Detection (& Interpretation)- Judgement & Pondering- Adaptivity- Goal orientation
© 2018, ANDATA
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A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Example for Desired Actions of Autonomous Braking System
Time/track diagram for constant,straight drive• When does one need to trigger a
certain action to avoid a collision?• Which action must be triggered at
time t to avoid a collision?
© 2018, ANDATA, several granted and pending patents
v1
v2
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A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Example Based Representation of Functional Requirements
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WarningBraking
WarningBraking
WarningBraking
Control Unit
ControlAlgorithm
Sensor signals Actorics
Situation1
Situation2
Situationn
.
.
.
© 2018, ANDATA, several granted and pending patents
A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Scenario-Management and Development/Approval of Actions
© 2018, ANDATA, several granted and pending patents
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A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Folding Various Decision Variables (e.g. collision probabilities)
© 2018, ANDATA, several granted and pending patents
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A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Var
.Lab
ellin
gs
Variation of Systems/ComponentsVariation of Situations / Conditions
Variation of ActionsVariation of Behaviours
VERONET Solution Concept for Automated Driving and Traffic
© 2018, ANDATA, several granted and pending patents
Scenario Catalog
Simulations
Naturalistic Driving
Connected Mobility
Test Fields
Vehi
cles
Traf
fic
Arti
ficia
lInt
ellig
ence
Big
Dat
a A
naly
tics
Rob
ustn
ess
Mng
t
Com
plex
ityM
ngt
Effe
ctiv
enes
sR
atin
g
Ref
eren
ce S
yste
ms
Sys
tem
-of-S
yste
ms
Con
flict
Ana
lysi
s
Con
d.P
roba
bilis
tics
Ø Quick Identification and Resolution ofRequirement Conflicts
Ø System UnderstandingØ Conform Specification of ComponentsØ Realistic Performance RatingsØ Excellent Control Algorithms
The Data Cube(s)for Vehicle and
Traffic Automation
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A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Adaption
Operation
Development/Training
Procedure for „Connected Development“
x1x2x3…xn
x y
Sensors &Information Action
y1y2y3…yn
xn+1x y
yn+1
Anomalie-Detection
Offl
ine
Onl
ine
Ser
ver/O
ffice
Clie
nt/O
nsite
Bac
kend
Fron
tend
Ø Collectively Learning Systems
FunctionAlgo
FunctionAlgo
yn+1
Rob
ustn
ess
Man
agem
ent
© 2018, ANDATA, several granted and pending patents
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A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
“Intelligent Systems” for Traffic Automation
© 2018, ANDATA
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Criteria MethodsDetection & Interpretation • Machine/Deep Learning
• SensorFusion
Judgement & Pondering • ScenarioCatalog• Effectiveness Rating• Robustness/Complexity Rating
Adaptivity • Machine Learning• Anomalies & Incident Detection• Co-Learning
Goal-Orientation • Consistent Rating and Assessment Functions• Optimization/Evolution
ü
ü
ü
ü
A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Example Goal-Orientation for Platooning(cooperative, connected, (semi)automated driving)
© 2018, ANDATA
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Avoidance ofAccidents
Improvementsfor Traffic
SavingFuel/Energy
Improve DriverConditions
ImproveLogistics
A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
Conclusion
© 2018, ANDATA
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• Necessary components for
intelligent systems are known and
available
• Steady improvements of
components are on the way
• Clever combinations and
extensions are still to be done
• Goals and control targets have to
be engineered/designed very well
A N D A T AA N D A T A
Kickoff Mobility of the Future, Call Nr. 11, Vienna, 2018-06-04
A N D A T A GmbHDr. Andreas KuhnTel: +43 6245 74063Email: [email protected]: www.andata.at
Thanks, for listening!
The singularity is near, let‘s be prepared!