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Accelerating AV Productizationwith AI
Danny Atsmon - CEO, CognataSimon Berard – CATIA Strategy Senior Manager, Dassault Systemes
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Agenda
● Vision
● Challenges
○ AI Solutions in Design
○ AI Solutions in Validation
● Solution Convergence
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AI-driven Design and Validation Solutions
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Vision
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Design Challenges
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3x more System
Requirements
Challenge: Mission Driven Engineering
Autonomous
SystemsTraditional Vehicles
Taxi DriverDeliveryPeople
Mission left to the User Mission engineered within the System
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Costs vs Complexity – AI is making it possible
Functional Requirements
AUTONOMOUSVEHICLES
__________
Leverage Patrimony
MASS-CUSTOMIZED
CARS__________
Explore Variants
MOBILITYEXPERIENCE
__________
Business Driven Innovation
COMPONENTS__________
Structure Legacy
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AI is bringing Quality
Functional Requirements
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Challenge 2: Physically Exact
Multidiscipline, Multiphysics, Multiscale
Consistent System Experience ValidationSensors optimization
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AI Solutions for Design
Learning from Patrimony
Context Sensitive
Automated Assembly
Parameters space
Exploration
Function Driven
Generative Design
Model Based System
Engineering
Performance
Tradeoff
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Validation Challenges
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AV Tech Has Had Some Unplanned Setbacks
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Challenge #1: Scale
Functional Requirements
Le
vel o
f A
uto
no
my
L0:INFORM
__________
Blind Spot
Lane Warning
Park Assist
L1: ASSIST__________
Adaptive Cruise Control
L2: ASSIST__________
Adaptive Cruise Control
+Lane
Centering
L2+: ASSIST__________
Highway Chauffeur
L4: DRIVE__________
Driverless Taxi
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Challenge #1: Scale - Need Vs. Actual
The Need: 11B*
*Rand corporation
Actually driven: 16M (0.15%)
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Challenge #2: Realism
REAL LIFE SIMULATIONVS.
Realism
100%
We need a realistic simulationfor a meaningful coverage
Scalable Realism
??%Scalable
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Realism - Uncanny Valley (Masahiro Mori, 1978)
16
Completely machine like
EM
OT
ION
AL
RE
SP
ON
SE
+
-
Fam
iliar
ity
50% 100%
UNCANNYVALLEY
Human likeness
Moving
StillFully human
Industrial robotStuffed animal
Bunarkupuppet
Polar Express
Humanoid robot
Zombie Prosthetic handSimulation today
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Realism - Uncanny Valley
17
Completely machine like
EM
OT
ION
AL
RE
SP
ON
SE
+
-
Fam
iliar
ity
50% 100%
UNCANNYVALLEY
Human likeness
Moving
StillFully human
Industrial robotStuffed animal
Bunarkupuppet
Polar Express
Humanoid robot
Zombie Prosthetic hand
Simulation needs tobe here
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The animation solution
18
- Walt Disney
“An animator cannot capture all
of reality . Instead he picks 3 or
4 distinct elements and
exaggerate them.“
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AI based methods for Realism
Nvidia & MIT - Video (Labels) to Video Synthesis – Wang et. al. 2018
END TO END
Procedural Modeling of a Building from a Single Image – Nishida et al, 2018
LAYERED APPROACH LAYERED APPROACH
Learning from Synthetic Humans – Varol et. al 2017
Realistic, not consistent Consistent, Not scalable (Manual)
Consistent, Not scalable (Variations)
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Challenge #2: Realism conclusions
REAL LIFE SIMULATIONVS.
• DNN transfer functions ~= Realism
• Isolated layers brings better results than End to End
• Data sources should be wide (crowd sourced)
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Cognata - 4 Technology layers
21
STATIC DYNAMIC SENSING CLOUD & ANALYTICS
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A Combined Solution
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Design and Validation Converge
Better Together
● Tests and designs together
● Validation base directly from
requirements
● Smart coverage
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Takeaways
● Autonomous vehicles brings○ New designs and use cases
○ Large scale validation challenge
● AI is a key to get Autonomous vehicles in a safe and cost effective way
● A platform solution to manage, design and validate is the needed solution
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Thank You!
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End to end limitations
26
The Problem: Not consistent, overfeat
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Buildings reconstruction conclusions
27
The Good: Consistent, Procedural
The Bad: Not practical
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Learning pose - Conclusions
28
This the most advanced way of learning moving objects.
The Good: Consistent
The Bad: Not scalable