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Accelerating AV Productizationwith AI
Danny Atsmon - CEO, CognataSimon Berard – CATIA Strategy Senior Manager, Dassault Systemes
Agenda
● Vision
● Challenges
○ AI Solutions in Design
○ AI Solutions in Validation
● Solution Convergence
AI-driven Design and Validation Solutions
Vision
Design Challenges
3x more System
Requirements
Challenge: Mission Driven Engineering
Autonomous
SystemsTraditional Vehicles
Taxi DriverDeliveryPeople
Mission left to the User Mission engineered within the System
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
AI is bringing Quality
Functional Requirements
Challenge 2: Physically Exact
Multidiscipline, Multiphysics, Multiscale
Consistent System Experience ValidationSensors optimization
AI Solutions for Design
Learning from Patrimony
Context Sensitive
Automated Assembly
Parameters space
Exploration
Function Driven
Generative Design
Model Based System
Engineering
Performance
Tradeoff
Validation Challenges
AV Tech Has Had Some Unplanned Setbacks
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
Challenge #1: Scale - Need Vs. Actual
The Need: 11B*
*Rand corporation
Actually driven: 16M (0.15%)
Challenge #2: Realism
REAL LIFE SIMULATIONVS.
Realism
100%
We need a realistic simulationfor a meaningful coverage
Scalable Realism
??%Scalable
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
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
The animation solution
18
- Walt Disney
“An animator cannot capture all
of reality . Instead he picks 3 or
4 distinct elements and
exaggerate them.“
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)
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)
Cognata - 4 Technology layers
21
STATIC DYNAMIC SENSING CLOUD & ANALYTICS
A Combined Solution
Design and Validation Converge
Better Together
● Tests and designs together
● Validation base directly from
requirements
● Smart coverage
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
Thank You!
End to end limitations
26
The Problem: Not consistent, overfeat
Buildings reconstruction conclusions
27
The Good: Consistent, Procedural
The Bad: Not practical
Learning pose - Conclusions
28
This the most advanced way of learning moving objects.
The Good: Consistent
The Bad: Not scalable