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Autonomous Driving: Need for AI and New Architectures
March 25, 2019
Luca De Ambroggi: Senior Director Transformative Technology, IHS Markit
© 2018 IHS Markit. All Rights Reserved.
OUTLINE
• AI pervasiveness
• Vehicle and Semiconductor market forecast
• Role of SoCs and AI-based systems in future cars
• Why AI in Automotive
• New E/E architectures required
• Growth of electronic content and costs by autonomy levels
• Software: a noble gas
• Open challenges and opportunities
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© 2018 IHS Markit. All Rights Reserved.
AI is pervasive: everywhere at the Edge and at the Cloud
Thousands of
Nodes
Millions of
Nodes
Billions of
Nodes
CLOUD
Data Centers
EDGE
End-Point
Devices
EDGE
Network
Computing
La
ten
cy
Pro
ce
ssin
g p
ow
er
En
erg
y c
on
su
mption
Development
requirements
for AI solutions
Priva
cy a
nd
se
cu
rity
Cost in
da
ta c
om
mu
nic
ation
Cybersecurity
Monitoring diagnostic
Predictive maintenance
Quality and testing
Genome & chemistry simulation
Retail & CRM
Autonomous machine
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Different Metrics for Investment and Research engagement:
4
Different Metrics
Different Strategy
Different Companies
> Business Case: Investment (Legacy Equipment, Process node and Product Life cycle) and ROI
– Market Volumes (i.e. Shipment) vs Time frame
– Revenue Expectation
– Market Momentum & Growth expectation
– Margin
– Technology Readiness
– Competition analysis
> Regulatory needs/assessment
> Eco-system development and needs (Partner and M&A)
– Start-up and new players
> System Complexity (HW and SW)
– Memory and Interfaces
– Key performance parameter:
• Processing/Data workload, Latency, Power, Safety
– Silicon differentiation in data-processing: GPU, CPU, TPU…up to Heterogeneous SoC;
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Vehicle production rises slowly
5
0
20
40
60
80
100
120
2015 2016 2017 2018 2019 2020 2021 2022 2023
Au
tom
oti
ve V
eh
icle
Pro
du
cti
on
(M
illio
ns o
f U
nit
s)
Middle
East/Africa
South America
South Asia
Japan/Korea
North America
Greater China
Europe
1.5% CAGR (2017 – 23)
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Electrification, automated driving and connectivity Fueling automotive semiconductor growth
6
8.3% CAGR (2017 – 24)
300
350
400
450
500
550
600
0
10
20
30
40
50
60
70
2016 2017 2018 2019 2020 2021 2022 2023 2024
Au
tom
oti
ve s
em
ico
nd
ucto
r r
even
ue
(B
illio
ns o
f U
S $
)
Powertrain
(ICE+H/EV)
Infotainment &
Telematics
ADAS
Body &
Convenience
Chassis & Safety
Other Automotive,
Trucks, AM
Avg. Semiconductor
Value
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Implication of AI and Deep LearningMajor advantages in comparison with traditional machine vision
• Assumptions:
> New silicon solutions will be developed with focus on AI algorithm
> The functional safety aspect will be addressed by the entire supply chain
• Deep learning can:
> Allow detection and recognition of multiple object ➔ improve perception
> Perform semantic analysis of the area surrounding the vehicle
> Reduce development time of ADAS and IVI systems (once DL is in steady-state)
> Reduce the power required compared to the same operation w/ traditional algorithms
• Deep Learning needs help
> Recognition/Prediction of actions and Fusion - Bayesian Net and other stochastic algorithms may complement DL in the run to autonomous cars (L4-L5)
• Required precondition:
> Telematics will be broadly deployed to: 1) enable gathering of “real” patterns and data for training 2) allow over the air system update and security
7
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Extra Requirements for Deep Learning in ADAS & AV
• DL in ADAS for Autonomous functions requires in-vehicle HW:
> Latency: for active function system needs to react in less than 70-80ms
> Deep Learning offer deterministic latency also for “noisy” input from sensors
> Performance: TFlop/TOP/TMAC is barely the minimum
• Power:
> Individual sensor subsystems need to stay in the power budget of 4W;
> Sensor Fusion ECUs might allow targets up to 15-20W or more. Some OEMs expect already they need to find a trade off if no silicon is available and performance needed.
• Backhaul and data storage infrastructure:
> Connectivity (IoT) is a need to:
– Store training data and vehicle parameters.
– Update/Upgrade the system
• Data acquisition is a challenge for validation and test: mix Real & Synthetic data (Simulation)
• Safety is the biggest uncertainty to have autonomous car based on AI.
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Standardisation is a must have
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Evolution of human machine interface (HMI)
StoneAge
Buttons & knobs Displays & more displays• Resistive displays
• Basic speech recognition
DisplayEra
DisplaysEverywhere
DigitalAssistance
Displays out, audio in• Capacitive displays
• Advanced speech recognition
• Speech-to-text and text-to-speech
Advanced audio & gestures• Natural voice recognition
• Interpret human speech and gestures
• Enable tasks and services
• HD and >10” displays
What pushed the changes?
• Diverse technologies: from displays to AI
• Lowered costs
• Influence of consumer electronics
• Increased customer needs
Why human machine interface?
• High added value and brand differentiation
• Limited performance and cost
• Steady algorithms
• Available data
• Not safety critical
• Ubiquitous across industries
AI
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Cost, Performance and Power for Autonomous Vehicles: still far from high volumes vehicle production
10
• Power consumption is critical in automotive➔ where can compromises be acceptable?
• Performance 50<TOPS<100 looks to be the target in L4-L5 ➔ is silicon today able to cope with it and ensure a long term roadmap?
• Sensor and ECU add up to several thousands of dollars➔ what is the Business Model?➔ where is the ROI?
• ISO26262 is the biggest challenge considering ML deployment
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AI in Automotive: Infotainment and ADAS
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0
50
100
150
200
250
2017 2018 2019 2020 2021 2022 2023 2024 2025
Millions o
f U
nits
Shipment of systems with AI functions
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ADAS Modules L3 L4 L5
Ultrasonic Sensors 12 12 12
Long-range Radar 1 1-2 1-2
Short/Mid-range Radar 2-4 2-6 4-6
Exterior Camera 5-8 12 8-15
Interior Camera 1 1 1
Night Vision Camera 0-1 0-1 0-1
Long-range Lidar 1 1 0-2
Short-range Lidar 0-2 0-4 0-4
ADAS Domain Controller 0-1 0 0
Autonomous Driving DC 1 1-2 2
TOTAL (without ultrasonic) 14 22 25
Typical ADAS content from level 3 to 5
Radars
Cameras
Lidars
ECUs
~ $2-3k ~ $3-6k ~ $6-9k
*Architectures based on existing pilot car platforms from BMW, Volvo, Audi, Nissan..
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Possible architecture for L4/L5 in model year 202x
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Asymmetric redundancy
© 2019 IHS MarkitSource: IHS Markit
• No Driver - Redundancy for L4/L5 is key
> Two identical (or nearly) Domain Controllers
• Provides complete redundancy.
• Expensive but comprehensive.
• DC2 can either share normal operations with DC1 or act just a back up.
• Redundant network and power supply
> Limited or distributed redundancy
• Cockpit Domain Controller (CDC) and/or Front View Camera are candidates because of their processing capabilities.
• Lower cost than symmetric redundancy but maybe less comprehensive.
*DC= Domain Controller
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ADAS classification by passive warning and active control
0 2 4 6 8 10 12
Volvo XC90
Tesla Model S
Mercedes-Benz E-Class
BMW 7-Series
Audi A8
Number of ADAS functions
Active Control Functions Passive Warning Functions
ADAS functional classification on 5 models
© 2017 IHS MarkitSource: IHS Markit
*The results of Tesla Model functions will be updated after investigation completes
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ADAS system cost to OEM by component value on Audi A8
$0 $100 $200 $300 $400 $500 $600 $700 $800 $900
12x Ultrasonic Parking
5x Basic Camera
Driver Monitoring Camera
Long-range Radar
Mono-camera Module
4x Mid-range Radar
LIDAR
Night Vision System
Domain Controller
Software value ($) SoC Value ($) Other Semiconductor Value ($) Other Component Value ($) Tier-1 Margin ($)
ADAS system cost to OEM by module and components
© 2017 IHS Markit
AD
AS
mo
du
le t
yp
e
ADAS system cost to OEM
Source: IHS Markit
IHS Markit TECHNOLOGY - Advanced ADAS Architecture Strategies
*Price of SOC does not include software value.
© 2018 IHS Markit. All Rights Reserved.
Thanks for your attention
Senior Director Transformative Technology - AI
URL:https://technology.ihs.com/Research-by-Market
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