| 1. |
EXECUTIVE SUMMARY |
| 1.1. |
IDTechEx Autonomous Car Report |
| 1.2. |
The Components of Autonomy |
| 1.3. |
SAE Levels of Automation in Cars |
| 1.4. |
Mixed Impact of COVID-19 in Autonomous Vehicles |
| 1.5. |
Impact of COVID-19 on the Automotive Market |
| 1.6. |
Legislative Barriers for Private Autonomous Vehicles |
| 1.7. |
Legislation Breakdown by Region |
| 1.8. |
Forecasting Adoption of Level 3 and 4 Technology |
| 1.9. |
Sensor Requirements for Different Levels of Autonomy |
| 1.10. |
Important Trends in the Sensor Holy Trinity |
| 1.11. |
Autonomy Will Bring Unparalleled Road Safety |
| 1.12. |
Autonomous MaaS has Arrived |
| 1.13. |
MaaS Market Entry by Region |
| 1.14. |
MaaS Adoption Forecast |
| 1.15. |
Car Sales Will Peak in the Early 2030s |
| 1.16. |
Forecasted Car Sales by Region 2022- 2042 |
| 1.17. |
Car Sales Broken Down by SAE Level |
| 1.18. |
How Will Level 4 Progress to level 5? |
| 1.19. |
Sensors Market Revenue ($) Forecast |
| 1.20. |
MaaS Global Market Revenue ($) Forecast: 2021-2041 |
| 1.21. |
Conclusions |
| 1.22. |
14 IDTechEx Portal Profiles |
| 2. |
INTRODUCTION |
| 2.1. |
Why Automate Cars? |
| 2.2. |
The Automation Levels in Detail |
| 2.3. |
Functions of Autonomous Driving at Different Levels |
| 2.4. |
Roadmap of Autonomous Driving Functions |
| 2.5. |
Typical Sensor Suite for Autonomous Cars |
| 2.6. |
Sensors and their Purpose |
| 2.7. |
Evolution of Sensor Suite from Level 1 to Level 4 |
| 2.8. |
Two Development Paths Towards Autonomous Driving |
| 2.9. |
Autonomy is Changing the Automotive Supply Chain |
| 2.10. |
Future Mobility Scenarios: Autonomous and Shared |
| 2.11. |
Privately Owned Autonomous Vehicles |
| 2.12. |
Mobility as a Service |
| 2.13. |
COVID-19 as a Driver for Autonomy in MaaS |
| 2.14. |
Semiconductor Content Increase in AVs |
| 2.15. |
Semiconductor Content Increase in EVs |
| 2.16. |
COVID-19 as a Barrier to Autonomy |
| 3. |
REGULATORY & LEGISLATIVE PROGRESS |
| 3.1. |
EU Mandating Level 2 Autonomy from July 2022 |
| 3.2. |
Privately owned Autonomous Vehicles |
| 3.3. |
Legislation and Autonomy |
| 3.4. |
Level 3, Legislation, UK, Europe and Japan |
| 3.5. |
The European Commission’s Roadmap to Autonomy |
| 3.6. |
Level 3, Legislation, US |
| 3.7. |
Level 3, Legislation, China |
| 3.8. |
The Autonomous Legal Race |
| 4. |
PRIVATE AUTONOMOUS VEHICLES |
| 4.1. |
Emerging Level 2+ Terminology. |
| 4.2. |
Sensor Suite Disclaimer |
| 4.3. |
Audi |
| 4.4. |
Audi A8 – Sensor suite |
| 4.5. |
Honda |
| 4.6. |
Honda Legend – Sensor suite |
| 4.7. |
Tesla |
| 4.8. |
Tesla Autopilot – Sensor suite |
| 4.9. |
General Motors (GM) |
| 4.10. |
Cadillac Escalade – Sensor suite |
| 4.11. |
General Motors – Precise GNSS localisation |
| 4.12. |
Daimler/Mercedes |
| 4.13. |
Mercedes S-class – Sensor Suite |
| 4.14. |
Daimler/Bosch Autonomous Parking |
| 4.15. |
BMW |
| 4.16. |
BMW iX – Sensor Suite. |
| 4.17. |
Ford **Ford skipping level 3** |
| 4.18. |
Ford/Argo AI – Sensor suite |
| 4.19. |
Volkswagen **Skipping level 3** |
| 4.20. |
Volkswagen ID.Buzz – Sensor Suite |
| 4.21. |
Toyota/Lexus |
| 4.22. |
Lexus LS and Toyota Mirai |
| 4.23. |
Renault/Nissan/Mitsubishi alliance |
| 4.24. |
Nissan ProPilot 2.0 – Sensor Suite |
| 4.25. |
Hyundai/Kia |
| 4.26. |
PSA |
| 4.27. |
PSA’s self driving sensor suite |
| 4.28. |
FCA, Fiat Chrysler Automobiles |
| 4.29. |
Huawei and Arcfox |
| 4.30. |
Arcfox Alpha S – Sensor suite |
| 4.31. |
Xpeng |
| 4.32. |
Xpeng P5 – Sensor suite |
| 4.33. |
BYD |
| 4.34. |
BYD Han – Sensor suite |
| 4.35. |
Geely (parent company of Volvo) |
| 4.36. |
Geely Xing Yue L – Sensor suite |
| 4.37. |
Changan |
| 4.38. |
Changan UNI-T – Sensor suite |
| 4.39. |
Leaders |
| 4.40. |
Sensor suite meta-data |
| 4.41. |
Sensors in privately owned autonomous vehicles |
| 4.42. |
Summary of Privately Owned Autonomous Vehicles |
| 5. |
MOBILITY AS A SERVICE (MAAS) |
| 5.1. |
MaaS Level 4 is Different From Privately Owned Level 4 |
| 5.2. |
Robotaxis & Robot Shuttles |
| 5.3. |
When Will Level 4 MaaS Be Ready? |
| 5.4. |
Who Are The Top 3 Performers? |
| 5.5. |
Testing – Impact From COVID-19 |
| 5.6. |
Testing |
| 5.7. |
Best Performers In 2020 By Disengagements (US) |
| 5.8. |
DMV Collision Report Analysis |
| 5.9. |
Driverless Testing Timeline |
| 5.10. |
Waymo |
| 5.11. |
Waymo Sensor Suite |
| 5.12. |
Waymo – Covid Response |
| 5.13. |
Waymo’s Strategic Partnerships |
| 5.14. |
Cruise |
| 5.15. |
Cruise Sensor Suite. |
| 5.16. |
Cruise – Covid Response |
| 5.17. |
AutoX |
| 5.18. |
AutoX Sensor Suite |
| 5.19. |
AutoX – Covid Response |
| 5.20. |
Baidu/Apollo |
| 5.21. |
Baidu/Apollo Sensor Suite |
| 5.22. |
Baidu – Covid Response |
| 5.23. |
Pony.ai |
| 5.24. |
Pony.ai Sensor Suite |
| 5.25. |
Pony.ai – Covid Response |
| 5.26. |
WeRide |
| 5.27. |
WeRide Sensor Suite |
| 5.28. |
DiDi |
| 5.29. |
DiDi Sensor Suite |
| 5.30. |
Aurora |
| 5.31. |
Aurora Sensor Suite |
| 5.32. |
Aurora – Covid Response |
| 5.33. |
Zoox |
| 5.34. |
Zoox Sensor Suite |
| 5.35. |
Zoox – Covid Response |
| 5.36. |
Motional, Aptiv & Lyft |
| 5.37. |
Motional & Aptiv Sensor Suite |
| 5.38. |
Yandex Launched Robotaxi Service in Russia |
| 5.39. |
Motional/Aptiv/Lyft – Covid Response |
| 5.40. |
Others |
| 5.41. |
Company Maturity |
| 5.42. |
MaaS Sensor Analysis |
| 5.43. |
MaaS Sensor Suite Analysis. |
| 5.44. |
Level 4 or level 5? |
| 6. |
ENABLING TECHNOLOGIES: LIDAR, RADAR, CAMERAS, INFRARED, HD MAPPING, TELEOPERATION, 5G AND V2X |
| 6.1.1. |
Connected vehicles |
| 6.1.2. |
Localisation |
| 6.1.3. |
AI and Training |
| 6.1.4. |
Teleoperation |
| 6.1.5. |
Cyber security |
| 6.2. |
Autonomous Vehicle Sensors |
| 6.2.1. |
The Sensor Trifactor |
| 6.2.2. |
How Many Sensors are Needed? |
| 6.2.3. |
Sensor Performance and Trends |
| 6.2.4. |
Robustness to Adverse Weather |
| 6.2.5. |
Evolution of Sensor Suite From Level 1 to Level 4 |
| 6.2.6. |
What is Sensor Fusion? |
| 6.2.7. |
Autonomous Driving Requires Different Validation System |
| 6.2.8. |
Sensor Fusion Technology Trends for Applications |
| 6.2.9. |
Hybrid AI for Sensor Fusion |
| 6.2.10. |
Autonomy and Electric Vehicles |
| 6.2.11. |
EV Range Reduction |
| 6.2.12. |
The Vulnerable Road User Challenge in City Traffic |
| 6.2.13. |
Pedestrian Risk Detection |
| 6.2.14. |
Multi-Layered Security Needed For Vehicle System |
| 6.2.15. |
The Coming Flood of Data in Autonomous Vehicles |
| 6.2.16. |
Autonomous Vehicle = Electric Vehicle? |
| 6.2.17. |
Horizon Robotics: the Chinese Embedded AI Chip Unicorn |
| 6.3. |
IDTechEx sensor suite top choices |
| 6.3.1. |
What Sensors and Features are Needed for Each Level? |
| 6.3.2. |
Level 2: The Trifactor |
| 6.3.3. |
Level 2: Extras |
| 6.3.4. |
Level 3: The Trifactor |
| 6.3.5. |
Level 3: Extras |
| 6.3.6. |
Level 4 private: The Trifactor |
| 6.3.7. |
Level 4 private: Extras |
| 6.3.8. |
Level 4 MaaS: The Trifactor |
| 6.3.9. |
Level 4 MaaS: Extras |
| 6.4. |
Cameras |
| 6.4.1. |
RGB/Visible light camera |
| 6.4.2. |
Camera Requirements Level 1-4 |
| 6.4.3. |
CMOS image sensors vs CCD cameras |
| 6.4.4. |
Key Components of CMOS |
| 6.4.5. |
Front vs backside illumination |
| 6.4.6. |
Reducing Cross-talk |
| 6.4.7. |
Global vs Rolling Shutter |
| 6.4.8. |
TPSCo: leading foundry for global shutter |
| 6.4.9. |
Sony: CMOS Breakthrough? |
| 6.4.10. |
Sony: BSI global shutter CMOS with stacked ADC |
| 6.4.11. |
OmniVision: 2.µm global shutter CMOS for automotive |
| 6.4.12. |
Hybrid organic-Si global shutter CMOS |
| 6.4.13. |
Event-based Vision: a New Sensor Type |
| 6.4.14. |
What is Event-based Sensing? |
| 6.4.15. |
General event-based sensing: Pros and cons |
| 6.4.16. |
What is Event-based Vision? |
| 6.4.17. |
What does event-based vision data look like? |
| 6.4.18. |
Event Based Vision in Autonomy? |
| 6.5. |
IR Cameras |
| 6.5.1. |
Segmenting the Electromagnetic Spectrum |
| 6.5.2. |
IR Cameras |
| 6.5.3. |
The Need for NIR |
| 6.5.4. |
OmniVision: Making Silicon CMOS Sensitive to NIR |
| 6.5.5. |
Motivation For Short-Wave Infra-Red (SWIR) Imaging |
| 6.5.6. |
Why SWIR in Autonomous Mobility |
| 6.5.7. |
Other SWIR Benefits: Better On-Road Hazard Detection |
| 6.5.8. |
SWIR Sensitivity of Materials |
| 6.5.9. |
SWIR Imaging: Incumbent and Emerging Technology Options |
| 6.5.10. |
The Challenge of High Resolution, Low Cost IR Sensors |
| 6.5.11. |
Silicon Based SWIR Detection – TriEye. |
| 6.6. |
Quantum Dots as Optical Sensor Materials for IR, NIR, SWIR |
| 6.6.1. |
Quantum Dots as Optical Sensor Materials |
| 6.6.2. |
Quantum Dots: Choice of the Material System |
| 6.6.3. |
Other Ongoing Challenges |
| 6.6.4. |
Advantage of Solution Processing |
| 6.6.5. |
QD-Si CMOS at IR and NIR |
| 6.6.6. |
Hybrid QD-Si Global Shutter CMOS at IR and NIR |
| 6.6.7. |
Emberion: QD-Graphene SWIR Sensor |
| 6.6.8. |
Emberion: QD-Graphene-Si Broadrange SWIR sensor |
| 6.6.9. |
SWIR Vision Sensors: First QD-Si Cameras and/or an Alternative to InVisage? |
| 6.6.10. |
QD-ROIC Si-CMOS integration examples (IMEC) |
| 6.6.11. |
QD-ROIC Si-CMOS Integration Examples (RTI International) |
| 6.6.12. |
QD-ROIC Si-CMOS Integration Examples (ICFO) |
| 6.6.13. |
QD-ROIC Si-CMOS Integration Examples (ICFO) |
| 6.7. |
Radar |
| 6.7.1. |
Radar |
| 6.7.2. |
Radar – Radio Detection And Ranging |
| 6.7.3. |
Safety Mandated Features Driving Wider Radar Adoption. |
| 6.7.4. |
Occupant Detection |
| 6.7.5. |
SRR, MRR and LRR: Different Functions |
| 6.7.6. |
Range Requirements Progressing From ADAS to AV |
| 6.7.7. |
Automotive Radars: Frequency Trends |
| 6.7.8. |
Radar: Which Parameters Limit the Achievable KPIs |
| 6.7.9. |
Impact of Frequency and Bandwidth on Angular Resolution |
| 6.7.10. |
Radar’s Fatal Flaw |
| 6.7.11. |
Imaging Radar |
| 6.7.12. |
Continental ARS540 – Product |
| 6.7.13. |
ZF |
| 6.7.14. |
Mobileye |
| 6.7.15. |
Arbe |
| 6.7.16. |
Others |
| 6.7.17. |
Towards Autonomy: Increasing Semiconductor Use |
| 6.7.18. |
Lunewave – Chip Manufacturer |
| 6.7.19. |
Unhder – Chip Developer |
| 6.7.20. |
Vayyar – Chip Manufacturer |
| 6.7.21. |
The Choice of the Semiconductor Technology |
| 6.7.22. |
Benchmarking of Semiconductor Technologies for mm Wave Radars |
| 6.7.23. |
Radar Aesthetics, Form and Function |
| 6.8. |
LiDAR |
| 6.8.1. |
Automotive Lidar: SWOT Analysis |
| 6.8.2. |
3D Lidar: Market Segments & Applications |
| 6.8.3. |
3D Lidar: Four Important Technology Choices |
| 6.8.4. |
Comparison of Lidar, Radar, Camera & Ultrasonic sensors |
| 6.8.5. |
Automotive Lidar: Operating Process & Requirements |
| 6.8.6. |
Emerging Technology Trends |
| 6.8.7. |
Comparison of TOF & FMCW Lidar |
| 6.8.8. |
Laser Technology Choices |
| 6.8.9. |
Comparison of Common Laser type & Wavelength Options |
| 6.8.10. |
Beam Steering Technology Choices |
| 6.8.11. |
Comparison of Common Beam Steering Options |
| 6.8.12. |
Photodetector Technology Choices |
| 6.8.13. |
Comparison of Common Photodetectors & Materials |
| 6.8.14. |
106 Lidar Players by Geography |
| 6.8.15. |
Lidar Hardware Supply Chain for L3+ Vehicles |
| 6.8.16. |
Beam Steering Technology |
| 6.8.17. |
Mechanical Lidar Players, Rotating & Non-Rotating |
| 6.8.18. |
Micromechanical Lidar Players, MEMS & other |
| 6.8.19. |
Pure Solid-State Lidar Players, OPA & Liquid Crystal |
| 6.8.20. |
Pure Solid-State Lidar Players, 3D Flash |
| 6.8.21. |
Lidars per Vehicle by Technology & Common Configurations |
| 6.8.22. |
Lidar configuration diagrams: L3, L4 & L5 vehicles |
| 6.8.23. |
Average Lidar Cost per Vehicle by Technology |
| 6.9. |
Mapping and Localisation |
| 6.9.1. |
What is Localisation? |
| 6.9.2. |
Localization: Absolute vs Relative |
| 6.9.3. |
Lane Models: Uses and Shortcomings |
| 6.9.4. |
HD Mapping Assets: From ADAS Map to Full Maps for Level-5 Autonomy |
| 6.9.5. |
Many Layers of an HD Map for Autonomous Driving |
| 6.9.6. |
HD Map as a Service |
| 6.9.7. |
Who are the Players? |
| 6.9.8. |
Mapping Business Models |
| 6.9.9. |
Vertically Integrated Mappers |
| 6.9.10. |
HD Mapping with Cameras |
| 6.9.11. |
HD Mapping with Cameras |
| 6.9.12. |
DeepMap |
| 6.9.13. |
Civil Maps |
| 6.9.14. |
Semi- or Fully Automating the Data-to-Map Process |
| 6.9.15. |
Radar Mapping |
| 6.9.16. |
Radar Localisation: Navtech |
| 6.9.17. |
Radar Localisation: WaveSense |
| 6.10. |
Teleoperation |
| 6.10.1. |
Enabling Autonomous MaaS |
| 6.10.2. |
3 Levels of Teleoperation |
| 6.10.3. |
How remote assistance works – Zoox |
| 6.10.4. |
Remote assistance |
| 6.10.5. |
Remote Control |
| 6.10.6. |
Where is teleoperation currently used? |
| 6.10.7. |
Players |
| 6.10.8. |
MaaS vs Independent solution providers |
| 6.10.9. |
Ottopia’s Advanced Teleoperation |
| 6.10.10. |
Business Model of Ottopia |
| 6.10.11. |
Phantom Auto’s Teleoperation as Back-Up for AVs |
| 6.10.12. |
Phantom Auto Gaining Momentum in Logistics |
| 6.10.13. |
Halo – Skipping the Tedious Mucking About With Autonomy |
| 6.11. |
Connectivity: WiFi, 5G, 6G, LiFi |
| 6.11.1. |
Vehicle-to-Everything (V2X) |
| 6.11.2. |
Why V2X Matters for Autonomy |
| 6.11.3. |
Wi-Fi vs Cellular |
| 6.11.4. |
Why V2X Matters for Autonomy |
| 6.11.5. |
Comparison of Wi-Fi and Cellular based V2X |
| 6.11.6. |
Regulatory: Wi-Fi based vs Cellular V2X |
| 6.11.7. |
Standards for Communication |
| 6.11.8. |
V2X Technologies Across the World |
| 6.11.9. |
OEM Applications of Connected Technologies |
| 6.11.10. |
Cellular V2X Via Base Station or Direct Communication |
| 6.11.11. |
Cellular V2X Via Base Station |
| 6.11.12. |
Direct Communication for Cellular V2X |
| 6.11.13. |
Use Cases and Applications of Cellular V2X Overview |
| 6.11.14. |
Cellular V2X for Automated Driving Use Case |
| 6.11.15. |
Use Cases of 5G NR Cellular V2X for Autonomous Driving |
| 6.11.16. |
Cellular V2X for Automated Driving Use Case |
| 6.11.17. |
Case Study: Cellular V2X Testing at Millbrook Proving Ground in the UK |
| 6.11.18. |
Case study: 5G to Provide Comprehensive View for Autonomous Driving |
| 6.11.19. |
Case study: 5G to Support HD Content and Driver Assistance System |
| 6.11.20. |
Ford Cellular V2X from 2022 |
| 6.11.21. |
Progress so Far |
| 6.11.22. |
Landscape of Supply Chain |
| 6.11.23. |
5G for Autonomous Vehicles: 5GAA |
| 6.11.24. |
6G – The Next Generation of Communications |
| 6.11.25. |
LiFi – Too little, Too late? |
| 7. |
FORECASTS |
| 7.1. |
MaaS market entry by region |
| 7.2. |
Method: Growth seed and addressable market |
| 7.3. |
Global MaaS adoption forecast 2022-2042 |
| 7.4. |
Shared and Private AV Forecast 2022-2042 |
| 7.5. |
Adoption of autonomous MaaS by region 2022-2042 |
| 7.6. |
Comparison to housing market |
| 7.7. |
Methodology for Forecasting Car Sales |
| 7.8. |
Car Sales Forecast 2015-2042, Peak Car |
| 7.9. |
Car Sales by Region Forecast 2015-2042 |
| 7.10. |
Private and Shared Car Sales Forecast by Region |
| 7.11. |
Private and Shared car sales by region (tabulated) |
| 7.12. |
Forecasting adoption of level 3 and level 4 technology |
| 7.13. |
Car Sales Forecast by SAE Level, 2015-2042 |
| 7.14. |
Car Sales Forecast by SAE Level, 2022-2042 |
| 7.15. |
Private Level 4 Sales Revenue Forecast 2022-2042 |
| 7.16. |
Sensors forecast – Radar |
| 7.17. |
Sensors market revenue ($) forecast |
| 7.18. |
Method – Mobility as a service revenue forecast |
| 7.19. |
MaaS global market revenue ($) forecast: 2021-2041 |