XCube
Accelerating Autonomous Driving
Development
February 2017
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To the Winner go the Spoils
• Autonomous driving (AD) is a high-stakes race between
– Disrupters (Waymo (Google), Tesla, NuTonomy, Uber) and
– Incumbents (GM, Toyota, et al and Tier 1 and 2 suppliers)
• The same skills are needed for ADAS
• At stake are trillions of dollars in market share*
That’s why:
– GM bought Cruise for $1 billion in March 2016
– Ford bought Argo for $1 billion in February 2017
*ADAS will be $132 billion a year by 2026. ABI Research, 2016.
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Advanced Driver Assistance Systems (ADAS)
Autonomous Driving
Curve speed adaptation
Collision avoidance
Lane departure
warnings
Automated
braking
Parking Automation
Blind spot
warnings
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Autonomous Driving (AD) and ADAS both require
collecting massive datasets
Maps
Software
Data Sets
Information
Modification
Sensor Data
Decision Making
Cameras
LIDAR
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Developing AD or ADAS software - simplified
Images
Maps
Sensor Data
Data Pool
Rules
Decision
Result
Application
Deep Learning / Neural Networks
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The Players
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Core Challenges…
Computer
1st Trainingtothe
Drive
2nd
Proving AD/ADAS Safe
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Training Challenges
Need millions of test drive scenes and variations
How can one find specific scenes in the petabytes of data in
multiple locations worldwide?
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Logic needs to be simulated under many conditions
These Include:
•
Road condition and type
•
Traffic conditions
•
Weather conditions
•
Lighting conditions
•
Object interactions
– Ball in the road
– Traffic signals
•
Other vehicle behaviors
– Brake lights two cars ahead would cause front car to break
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Why is it difficult?
•
Current tools are inadequate
– Hadoop and Spark require expensive and time consuming centralization
of data and a new application. Even if you are using newer data lakes,
however:
•
Deep learning algorithms incapable
– Of knowing what to test
– Of testing and reasoning about particular scenes
– Of being scaled in parallel
– Tedious to re-test similar scenes
“Machine learning systems, and deep learning in
particular, are very high performance but don’t
actually explain how they figure out the answer”
– Gill Pratt, CEO Toyota Research
Insight
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In sum, developing AD or ADAS software requires:
• Finding unusual situations and catastrophes in petabytes of data
• Simulating the unusual situations
• Acceleration of simulation in parallel
• Training deep learning neural networks in parallel on specific scenes
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XCube Toolset is Solving Those Challenges
Main software functions
Store
• Store hundreds of petabytes on
geographically distributed systems
Mine
• Tag content inside the data files
automatically
Search
Use
• Search the tagged content in parallel for
matching time intervals
• Run any desktop application (e.g.
simulator) in parallel
Manage
• Manage millions of search results and
billions of test results
Automate
• Automate regression testing, AI-training,
and proof of safe operation
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XCube Automates Machine Learning at Scale
•
Transforms problem of distributed data into the advantage of parallel
processing
•
Automates training and testing of neural networks in parallel
•
Allows granular testing of situations, such as rain, snow, or four-way
intersections
•
Vendor independent framework support
– Supports most popular frameworks natively (Caffe, Torch, Tensor Flow,
Matlab)
– NVidia, Intel, Xeon-Phi, new FPGA-CPU and other architectures
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Team
Satish Jha, Chairman &
CEO
Mikael Taveniku PhD,
President
• CIO, Roche Holding AG
• CEO, James Martin &
Company
• Seeded and mentored 35
startups
• EDHEC; Fletcher School of
Law and Diplomacy
27 years of leadership in:
• Ericsson, Mercury Systems
• High-end designs of EASA
radar, sonar, CT
• Machine learning advanced
applications
• PhD, MECS, BSEE, BSME
Yaron Naor, VP Business
Development
Shantanu Jha, Chief Product
Officer
Founder & CEO, Mobile
Wisdom, a ComverseSiemens JV
GM at Comverse
Commander (Ret.) Israeli
Defense Force
MBA, University of Aalto
• Portfolio Manager,
Beaconsfield Capital
Management
• CTO, Uni AI Software
• 10 years of deep learning
experience
• University of Chicago, LSE,
Cambridge
•
•
•
•
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XCube Initial Customers - Ongoing PoCs
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Current Use Cases
• Train the deep learning system for all situations
• Prove that the system is responsive, comprehensive and safe
• Find particular driving scenes from millions of hour of
recordings
• Automatically annotate data from video and other sensor
streams
• Run new algorithms in parallel on tens of thousands of
scenarios found, varying hundreds of parameters in each
scene
• New advanced algorithms with deep learning under
development
• Data and team distributed across the world
• Run applications based on distributed datasets from anywhere
• Very large scale simulations
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XCube Pipeline
OEMs
Tier 1 Suppliers
Tier 2 Suppliers
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Revenue growth – One Customer, One vehicle Model
$100K
3 – 9 month Pilot Program
$1M 1st year
3 month initial deployment
Year 1:Small Team
$5M / year
Vehicle testing on road
Year 2-4: Global team
Fleet test, verification
15% - 20%
Annual Maintenance
Model on Road
~Years 3 – 10
$15M - $20M Revenue Per Vehicle Model
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Financial projections
Today
Pro Forma Income
Statement ($M)
2016
2017
2018
2019
2020
(actual)
Revenue
1.1
3.5
12
27
55
COGS
0.6
1.7
5.0
12
25
Margin
0.5
1.8
8.0
15
30
R&D
0.3
1.0
3.5
7.0
10
SG&A
0.1
0.7
2.0
3.0
5.0
Net Profit
0.1
0.1
2.5
5.0
15
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Milestones
Secure funding
and hire additional
engineers / team
members
Feb
2017
June
2017
Secure at least 3
major deals with
OEMs and/or
Tier 1s
Q1
2018
Dec
2017
Complete
product
evaluation with
Delphi, Honda,
SL Corp and
Baidu
Win
additional
customers
and establish
office in
APAC
Q2
2018
Q3
2018
Expand
local
presence
in
Europe
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2 – 3 Year Exit Strategy
• Candidates
– Potential Users (SAIC, Hyundai)
– Prospects (Delphi, Volvo, Toyota, GM)
– Technology Partners (ÅF, NVidia)
• Comparable Exits
Cruise Automation ($1B, no revenue)
Automotive: Argo ($1B, no revenue)
Actuate ($330M)
Big Data:
Machine Learning:
Pentaho ($550M)
ColdLight ($105M, $8M in revenue)
AlchemyAPIàIBM
WhetlabàTwitter
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* Source JD Ford & Company
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Summary
Marquee
Customers –
Honda, Volvo,
Delphi, Baidu,
SAIC, Huayu
Unique Product –
State of the art
product automating
machine
learning/testing for
autonomous vehicles
Supportive
Investors - Beacon
Angels, Launchpad
Organic Growth Business
Model – becoming a long
term partner providing
scalable infrastructure
Stable Team – top tier in
technology, management,
and business development
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Thank You
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