Sensing and Computing for ADAS Vehicle 2020 - From Technologies to Markets - i-Micronews
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From Technologies to Markets
Sensing and
Computing for
ADAS Vehicle 2020
Market and Technology
Report
Sample
© 2020
TABLE OF CONTENTS
Part 1/4
Glossary and definitions 6 Market forecasts 69
o Initial statements
Report objectives 7 o Impact of COVID-19 on forecasts
Scope of the report 8 o Image sensors and camera modules forecast in Munits
o Image sensors market revenue forecast in $M
Report methodology 10 o Camera module market revenue forecast in $M
About the authors 13 o LiDAR volume and revenue forecast – Split by type
o Radar module volume and revenue forecast – Split by
Companies cited in the report 15 frequency
o Computing hardware volume and revenue forecast by
Related reports from the Yole group 16 segment
---------------------------------------------------------------- o Overview of sensors and computing market revenue
------------ Market trends 88
o The road to automated driving
Executive summary 17
o Different embedded sensor technologies
---------------------------------------------------------------- o Euro NCAP 2025 roadmap - in pursuit of ‘vision zero’
------------ o AEB is still perfectible
o Sensor complement per car segment
Context 43
o The ‘Ten-plus cameras per car’ roadmap
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 2
TABLE OF CONTENTS
Part 2/4
Market shares and supply chain 99 Technology trends 140
o Industry overview
o Camera
• Competitive landscape
• Device and technology segmentation
• Overview of players – Distribution by type of sensor
• Comparison of cameras for different applications
• C.A.S.E., the acronym taking over the auto industry
• Strategies to develop different sensor technologies • Inside a forward ADAS camera – Example: ZF S-Cam4 TriCam
Camera
• Next acquisition moves will be related to software,
and have already started • Forward ADAS cameras are becoming increasingly complex
o Industry trends • Side-mirror replacement application
• Recent partnership activity • Thermal cameras remains a high-end feature poised to move
• From sensors to fusion in automotive into ADAS
o Market shares • Driver monitoring – Possible use cases
• Automotive image sensors • Driver monitoring – Different approaches
• Automotive camera modules • Company profiles
• Automotive LiDAR o LiDAR
• Automotive radar • LiDAR principles and components
o Supply chain • LiDAR ranging methods
• Automotive image sensors • Lasers for automotive LiDAR
• Automotive camera modules
• Photodetectors for automotive LiDAR
• Automotive LiDAR
• Technology roadmap – Potential winners in the next five
• Automotive radar years?
• LiDAR integration in ADAS vehicles
• Size evolution of LiDAR
• Company profiles
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 3
TABLE OF CONTENTS
Part 3/4
Technology trends 178 Technology trends 201
o Radar o E/E architecture and computing
• Radar capabilities • Evolution of E/E architecture
• Overview of the different types of networks
• Which technology for which application?
• Comparison of automotive bus systems
• Main frequency bands • E/E architecture evolution – Key drivers
• Regional radar frequency allocation • E/E architecture evolution – Roadmap
• From assisted driving to automated driving • E/E architecture evolution – Domain centralized vs. vehicle
centralized
• Main components in a radar system
• The emergence of automotive Ethernet
• Four steps towards super sensors • Automotive Ethernet: the future of in-car networking
• The road to high resolution • Evolution of sensors: from smart to dumb sensors
• In-cabin presence detection, a fit for radar? • Computing unit – ADAS system overview
• Company profiles • Computing unit – vision processing
• ADAS implies more computing power
o Cost breakdown of sensors
• Data fusion for automated driving
• Camera teardown example: Denso camera • Difference between current and future cars
• LiDAR teardown example:Valeo LiDAR • Challenges regarding software in vehicles
• Radar teardown example: Aptiv radar • Security features will be required to prevent hacking of
vehicles
• Component cost comparison
• Future car architecture
• Component breakdown comparison
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 4
TABLE OF CONTENT
Part 4/4
Conclusion 246
Presentation of Yole Développement 248
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 5
GLOSSARY AND DEFINITIONS
• ACC Adaptive Cruise Control • FPGA Field-Programmable Grid Array
• AD Autonomous Driving
• GPS Global Positioning System
• ADAS Advanced Driver Assistance Systems
• LCA Lane-Change Assist
• AEB Automated Emergency Braking
• LCV Light Commercial Vehicle
• AES Automatic Emergency Steering
• LDW Lane-Departure Warning
• AV Autonomous Vehicle
• LiDAR Light Detection and Ranging
• ASIL Automotive Safety Integrity Level
• LKA Lane-Keep Assist
• ASP Average Selling Price
• LRR Long-Range Radar
• BSD Blind-Spot Detection
• MRR Mid-Range Radar
• CAGR Compound Average Growth Rate
• OEM Original Equipment Manufacturer
• CIS CMOS Image Sensor
• PC Personal Car
• CMOS Complementary Metal Oxide Semiconductor
• Radar Radio Detection and Ranging
• DM Driver Monitoring
• SAE Society of Automotive Engineers
• E/E Electrical/Electronic
• SRR Short-Range Radar
• ECU Electronic Control Unit
• TJA Traffic Jam Assist
• FCW Forward Collision Warning
• ToF Time of Flight
• FMCW Frequency-Modulated Continuous Wave
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 6
REPORT OBJECTIVES
1. Provide market data on key sensors e.g. cameras, LiDAR and radar.
o Revenue forecast and volume shipments, for each sensor type.
o Market shares with detailed breakdown by player.
o Application focus of each sensor.
2. Deliver an in-depth understanding of the main sensor value chain, infrastructure and players.
1. Who are the sensor players, and how are they related?
2. What is the supply chain for these sensors?
3. Present key technical insights and analysis regarding future technology trends and challenges.
1. Have a deep understanding of how these sensors work together in a car.
2. Analysis of the E/E architecture of a car and how it will evolve.
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 7
SCOPE OF THE REPORT - 1/2
Non/µpowered Public transport Light vehicle Air transport
Current transport
vehicles
ADAS
Robotic transport
vehicles
Urban air
Pods Shuttles Robo-taxi
mobility
Scope of the report Note: For more information on robotic vehicles, please see the Sensors for Robotic Mobility report 2020.
Out of scope
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 8
SCOPE OF THE REPORT - 2/2
Semiconductor Electronic Electronic ADAS
Supply chains
device Module System Vehicles
LiDAR LiDARLR
LiDAR LR
LiDARMR
LiDAR MR
Laser Diodes
diodes Fiber lasers LiDARSR
LiDAR SR
Radar Radar LRR
LR
Radar chips Radar modules Radar SRR
SR
Camera Camera LR
CMOS Image Sensors
image sensors Camera modules Camera SR
GNSS and IMU
RF and MEMs chips RTK modules GNSS
IMU &and IMU
GNSS
Computing
GPU – SoC – SiP Computing boards AD Computing
Note: ultrasonic sensors are not included in this report.
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 9
METHODOLOGIES & DEFINITIONS
Yole’s market forecast model is based on the matching of several sources:
Preexisting
information
Market
Volume (in Munits)
ASP (in $)
Revenue (in $M)
Information
Aggregation
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 10ABOUT THE AUTHORS
Biographies and contacts
Pierrick BOULAY
As part of the Photonics, Sensing and Display division at Yole Développement (Yole), Pierrick Boulay works as a market and technology
analyst in the fields of solid-state lighting and lighting systems, where he performs technical, economic and marketing analysis. Pierrick has
authored several reports and custom analyses dedicated to topics such as general lighting, automotive lighting, LiDAR, IR LEDs, UV LEDs
and VCSELs.
Prior to Yole, Pierrick worked in several companies where he developed his knowledge on both general lighting and automotive lighting. In
the past, he has mostly worked in R&D departments for LED lighting applications. Pierrick holds a master’s degree in Electronics from
ESEO in Angers, France.
Contact: pierrick.boulay@yole.fr
Cedric MALAQUIN
As a technology and market analyst specializing in RF devices and technologies at Yole, Cédric Malaquin is involved in the development
of technology and market reports as well as the production of custom consulting projects. Prior to working with Yole, Cédric was
employed at Soitec as a process integration engineer for nine years, and then as an electrical characterization engineer for six years.
Cédric has contributed heavily to FDSOI and RFSOI product characterization and has authored or co-authored three patents and five
international publications in the semiconductor field. Cédric graduated from Polytech Lille in France with an engineering degree in
Microelectronics and Material Sciences.
Contact: cedric.malaquin@yole.fr
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 11ABOUT THE AUTHORS
Biographies and contacts
Yohann TSCHUDI
As a software and market analyst, Dr. Yohann Tschudi is a member of the Semiconductor and Software division at Yole. Yohann works
daily with his team to identify, understand, and analyze the role of software and computing parts within any semiconductor product, from
machine code to the most advanced algorithms. Following his thesis at CERN in Geneva, Switzerland, Yohann developed dedicated
software for fluid mechanics and thermodynamics applications.
Afterwards, he served for two years at the University of Miami in FL, United-States as an AI scientist. Yohann has a PhD in High-Energy
Physics and a master’s degree in Physical Sciences from Claude Bernard University in Lyon, France.
Contact: yohann.tschudi@yole.fr
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 12COMPANIES CITED IN THIS REPORT
AGC, Algolux, Altera, Ambarella, ams, Apple, Aptiv, Argo, ARM, Audi, Aurora, Avis, Baidu, Blackmore,
Blickfeld, BMW, Bolloré, Bosch, BrightWayVision, Cambricon, Cepton, Chevrolet, Continental, Cruise,
Delphi, Denso, Didi, Dodge, Excelitas, EyeSight, Fiat, First Sensor, Flir, Ford, Freescale, Fujitsu, Geely, GM,
Google, Hella, Hitachi, Honda, Horizon Robotics, Hyundain Hyundai-Mobis, Ibeo, II-VI, Infineon, Innoviz,
Jabil, Jaguar, Kalray, Koito, Kostal, Land Rover, Laser Components, Lattice, LeddarTech, Lexus, Lumileds,
Luminar, Lumotive, Lyft, Magna, Marelli, Maxel, May Mobility, Mazda, Melexis, Mercedes, Metawave,
Micron, Mobileye, Nichia, Nidec, Nissan, Nvidia, NXP, Omnivision, OnSemiconductor, Osram, Ouster,
Panasonic, Peugeot, Pioneer, Pony.ai, Porsche, Qualcomm, Quanergy, Renault, Renesas, Robosense, SAIC,
Samsung, Seeing Machine, Seminex, Silc, Smart Eye, Sony, STmicroelectronics, Sunny Optical Technology,
Tesla, Texas Instrument, Toshiba, Toyota, Trieye, Trilumina, Trumpf, TSMC, Uber,Valeo,Velodyne,Veoneer,
Volkswagen,Volvo, Waymo, Xenomatix, Xilinx, Xperi, ZF, ZKW
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 13CONTEXT
C.A.S.E., the acronym taking over the auto industry
Shared
Autonomous Owning, sharing, or renting, the
Sensor suite and computing mobility of the future offers greater
developments for safer roads. flexibility.
Source: Daimler
Connectivity Electric
Comfort, safety and entertainment in Alternative drive systems to reduce
a new dimension. CO2 emissions.
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 14CONTEXT
Levels of autonomy – Differences between levels
Level 0 Level 1 Level 2 Level 3 Level 4 Level 5
Conditional
Manual driving Assisted driving Partial automation High automation Full automation
automation
• In defined use cases, the driver can transfer the driving task to the
• The driver is assisted in the driving task by
system.
The driver does the system.
• Side activities can be permitted.
everything. • The driver is not allowed to do secondary
• The driver has to take over within a specified time (level 3) or when he
tasks and keeps focusing on the road.
wants to leave the domain (level 4).
xxx
xxx xxx
xxx
Ultrasonic x8 xxx xxx
Ultrasonic x4 xxx
Radar LRR x1 xxx xxx
Radar LRR x1 xxx
Radar SRR x3 Radar MRR x4 xxx xxx
Radar SRR x2 xxx
ADAS camera x1 xxx xxx
Backup camera x1 xxx
Viewing camera x4 xxx xxx
xxx
xxx xxx
Computing power Computing power Computing power Computing power Computing power
-
< 0.25TOPS ~ 0.25TOPS ~xxxTOPS ~xxxTOPS? ~xxx TOPS?
Technological gap
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 15MARKET TRENDS
Technological roadmap for automotive sensors
Current New technology Massive
innovation introduction adoption ?
Front – Rear –
Imaging radar Long range radar
Front –
Sensor 3D radar Radar
technologies
continue to
improve. Driver Night vision
Radar monitoring penetration
technology 1-3 forward
seems to be ADAS cameras Camera
improving the
fastest.
Grill – LiDAR in
MEMS LiDAR headlamps?
Grill – macro-
mechanical LiDAR LiDAR
2019 2021 2023-2024
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 16SUPPLY CHAIN
Example: Audi A8
Suppliers Tier-1 System
Front
camera
An example of
supply chains
for the main Long-
sensors and range
domain radar
controller of
the Audi A8.
LiDAR
zFAS
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 17INDUSTRY OVERVIEW
Automotive imaging competitive landscape
Signal processing Sensor Automotive camera
Power management Lens suppliers suppliers manufacturers Tier-1s OEMs
Established players
New entrants
Sensing
Sensing and computing
and Computing for ADAS
for ADAS vehicles
Vehicle 2020 | Report
Sample | www.yole.fr | ©2020 18TECHNOLOGY TRENDS
The road to automated driving
Manual driving Automated driving
XXX
XXX Increasing
XXX software
XXX
XXX
XXX XXX
X M lines of code?
Engine controllers XXX 2025 Or more?
Xxx Engine controllers
Passive safety
XXX
Engine controllers Passive safety XXX
X M lines of code
XXX Body & Security 2020
Passive safety
Body & Security X M lines of code
Body & security 2010
1990 1M lines of code
2000 Yole Développement © April 2020
Domain expansion Domain integration
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 19LIDAR
Technology roadmap – Potential winner in the long term?
2025 ? 2030 Similarities:
Credits: Ibeo
xxx
905nm-based
systems should MEMS and flash LiDARs
xxx
continue to be
used to due to Credits: SOSLab
their low cost
but FMCW
LiDARs based
on 1550nm
Credits: Blackmore xxx
could emerge FMCW LiDARs
in the long Credits: Insight xxx
term. LiDAR
Suited for
1550nm Credits: Analog
Photonics
xxx
Credits: SILC
Technologies
OPA LiDARs
xxx
Credits: Voyant
Photonics
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 20RADAR
From assisted driving to automated driving
2015 2018 2025 2035
Radar will
improve in 24GHz/77GHz 79GHz/77GHz
range/angular
resolution and
shrink in cost
and size, 2 SRR 1 LRR 4 MRR/SRR 1 LRR
enabling the $60 $90 $45 $80
creation of a
‘safety cocoon’
120°/90m 120°/90m
around the car. 120°/50m
20°/250m
20°/250m
120°/50m 120°/90m 120°/90m
Level 0 - Level 1 - Level 2 Level 2++ Level 3 Level 4/5
Driver assistance Automated driving
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 21E/E ARCHITECTURE AND COMPUTING
E/E architecture evolution - Roadmap
Super - xxx
computer
2030-2035
Vehicle centralization
Development xxx
from a 2025
distributed
architecture Domain centralization
Yole Développement © April 2020
to a
centralized
architecture. xxx
2020
Distributed architecture
Increasing software amount
Xxx M lines of code Xxx M lines of code > Xxx M lines of code
• Today, OEMs are still using a distributed E/E architecture with roughly one ECU per function.
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 22E/E ARCHITECTURE AND COMPUTING
Data fusion for automated driving – 1/2
2030
2020 Lane Keeping Assist
Ultrasonic sensor
Lane Keeping Assist ACC with Stop & Go
Blind Spot Monitoring
ACC with Stop & Go
Radar
Parking Assist
Blind Spot Monitoring
High Beam Assist
Parking Assist ADAS camera
Traffic Sign Recognition
High Beam Assist
LiDAR AEB
Traffic Sign Recognition Parking valet
Distributed or domain Traffic Jam Pilot
centralized E/E architecture Viewing camera
Highway Pilot
And more functions
Thermal camera
Domain or vehicle centralized
E/E architecture
Data fusion Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 23MARKET FORECASTS
Camera module market revenue forecast in $M
Yole Développement © April 2020
Covid-19
impact
Camera
module sales
are expected
to reach $8B
in 2025.
Note: Night vision is
integrated in the
forecast.
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 24MARKET FORECASTS
LiDAR revenue forecast – Split by type
• Currently, only Audi
includes LiDARs from
Valeo in its cars as an
option.
• BMW will use MEMs
LiDAR LiDAR from Innoviz in Yole Développement © April 2020
revenue is low volumes, starting in
expected to 2021 and Volvo will use a
reach a total LiDAR from Luminar
starting in 2022.
of $1.7B in
2025 with a • We estimate that the
CAGR20-25 of take rate of this option
113%. will be quite low, between
9% and 15%, depending
on the model.
• Therefore, the market
will be dominated by
macro-mechanical LiDAR
in the short term.
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 25MARKET FORECASTS
Radar module market revenue forecast in $M
Yole Développement © April 2020
Covid-19
impact
Radar module
market is
expected to
reach $9B in
2025 and
growing at a
CAGR20-25 of
19%.
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 26MARKET FORECASTS
Computing ADAS revenue forecast in $M
Yole Développement © April 2020
Computing
ADAS market Covid-19
is expected to impact
reach $3.5B in
2025 and
growing at a
CAGR20-25 of
22%.
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 27YOLE GROUP OF COMPANIES RELATED REPORTS
Yole Développement
Imaging for AI Computing for Automotive Radar and Wireless for
Automotive: Market and
Automotive 2019 2020 (coming soon) Technology Trends 2019
Contact our
Sales Team
for more
information
Status of the Radar Industry LiDAR for Automotive and
2020 (coming soon) Industrial Applications 2019
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 28YOLE GROUP OF COMPANIES RELATED REPORTS
System Plus Consulting
Aptiv’s Third Generation of 77 Aptiv’s Lane Assist Front Tesla Model 3 Driver-Assist
GHz-Based Short-Range Radar
(SRR3) Camera for Audi A8 Autopilot Control Module Unit
Contact our
Sales Team
for more
information
The Audi A8 zFAS ADAS Valeo SCALA
Platform by Aptiv Laser Scanner
Sensing and Computing for ADAS Vehicle 2020 | Sample | www.yole.fr | ©2020 29CONTACTS
REPORTS, MONITORS & TRACKS
Western US & Canada India and RoA Japan
Steve Laferriere - steve.laferriere@yole.fr Takashi Onozawa - takashi.onozawa@yole.fr Miho Ohtake - miho.ohtake@yole.fr
+ 1 310 600 8267 +81 80 4371 4887 +81 34 4059 204
Eastern US & Canada Greater China Japan and Singapore
Chris Youman - chris.youman@yole.fr Mavis Wang - mavis.wang@yole.fr Itsuyo Oshiba - itsuyo.oshiba@yole.fr
+1 919 607 9839 +886 979 336 809 +86 136 6156 6824 +81 80 3577 3042
Europe and RoW Korea Japan
Lizzie Levenez - lizzie.levenez@yole.fr Peter Ok - peter.ok@yole.fr Toru Hosaka – toru.hosaka@yole.fr
+49 15 123 544 182 +82 10 4089 0233 +81 90 1775 3866
Benelux, UK & Spain
Marine Wybranietz - marine.wybranietz@yole.fr
+49 69 96 21 76 78
FINANCIAL SERVICES CUSTOM PROJECT SERVICES GENERAL
› Jean-Christophe Eloy - eloy@yole.fr › Jérome Azémar, Yole Développement - › Camille Veyrier, Marketing & Communication
+33 4 72 83 01 80 jerome.azemar@yole.fr - +33 6 27 68 69 33 camille.veyrier@yole.fr - +33 472 83 01 01
› Sandrine Leroy, Public Relations
› Ivan Donaldson - ivan.donaldson@yole.fr › Julie Coulon, System Plus Consulting - sandrine.leroy@yole.fr - +33 4 72 83 01 89
+1 208 850 3914 jcoulon@systemplus.fr - +33 2 72 17 89 85
› General inquiries: info@yole.fr - +33 4 72 83 01 80
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