1. | EXECUTIVE SUMMARY |
1.1. | IDTechEx Autonomous Car Report |
1.2. | SAE Levels of Automation in Cars |
1.3. | The Automotive Market is Now Recovering From COVID-19 |
1.4. | Legislative Barriers for Private Autonomous Vehicles |
1.5. | The Autonomous Legal Race |
1.6. | Progression of Level 0, Level 1 and Level 2 |
1.7. | Emergence of level 3 and Level 4 Technologies |
1.8. | Private Vehicle Leaders |
1.9. | When Will There be Level 5? |
1.10. | Robotaxis Now Approaching Human Levels of Safety |
1.11. | The Beginning of Commercial Robotaxi Services |
1.12. | State of development |
1.13. | Sensor Requirements for Different Levels of Autonomy |
1.14. | Sensor Suite Costs |
1.15. | Front Radar Applications |
1.16. | The Role of Side Radars |
1.17. | Vehicle camera applications |
1.18. | LiDARs in automotive applications |
1.19. | The Big Three Sensors |
1.20. | Autonomy is Changing the Automotive Supply Chain |
1.21. | Robotaxi Commercial Service market entry by region |
1.22. | Private and Autonomous Passenger Vehicle Mileage 2022-2044 |
1.23. | Robotaxi Service Revenue 2024-2044 |
1.24. | Global Vehicle Sales and Peak Car by Region 2019-2044 |
1.25. | Global Vehicle Sales and Peak Car by SAE Level 2022-2044 |
1.26. | Automotive Market Revenue by Region 2022-2044 |
1.27. | Sensors for Cars Revenue: 2022-2044 |
1.28. | 32 Company Profiles Including 26 Primary Interviews |
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 in Private Cars |
2.5. | Typical Sensor Suite for Autonomous Cars |
2.6. | Sensors and their Purpose |
2.7. | Evolution of Sensor Suites 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. | Robotaxis and Robotaxi Services |
3. | REGULATORY & LEGISLATIVE PROGRESS FOR PRIVATE |
3.1. | Introduction |
3.1.1. | Privately owned Autonomous Vehicles |
3.1.2. | Legislation and Autonomy |
3.2. | Europe |
3.2.1. | EU Mandating Level 2 Autonomy from July 2022 |
3.2.2. | Level 3 roll out in Europe (1) |
3.2.3. | Level 3 roll out in Europe (2) |
3.2.4. | Level 3 outlook in Europe |
3.2.5. | UNECE 2023 update |
3.3. | US |
3.3.1. | Level 3, Legislation, US |
3.3.2. | Mercedes S-Class first level 3 car in US |
3.3.3. | Outlook for the US |
3.4. | China |
3.4.1. | Level 3, Legislation, China |
3.4.2. | Shenzhen moves towards level 3 |
3.4.3. | Outlook for China |
3.5. | Japan |
3.5.1. | Private autonomous vehicles in Japan |
3.6. | World overview |
3.6.1. | The Autonomous Legal Race |
4. | PRIVATE AUTONOMOUS VEHICLES |
4.1. | ADAS Features |
4.1.1. | Emerging Level 2+ Terminology. |
4.1.2. | IDTechEx's ADAS Feature Database |
4.1.3. | ADAS Adoption by Region in 2022 |
4.1.4. | ADAS Feature Deployment in the US |
4.1.5. | ADAS Feature Deployment in the China |
4.1.6. | ADAS Feature Deployment in EU + UK + EFTA |
4.1.7. | ADAS Feature Deployment in Japan |
4.1.8. | SAE Level Adoption by Region 2020 vs 2022 |
4.1.9. | OEMs Ranked on ADAS Deployment |
4.2. | Examples and Case Studies |
4.2.1. | Sensor Suite Disclaimer |
4.2.2. | Honda |
4.2.3. | Honda Legend - Sensor suite |
4.2.4. | Mercedes S-Class (2021), EQS (2022) |
4.2.5. | Mercedes S-class - Sensor Suite |
4.2.6. | Daimler/Bosch Autonomous Parking |
4.2.7. | Ford, VW and Argo AI |
4.2.8. | Audi |
4.2.9. | Case study - Audi A8 (2017) |
4.2.10. | Tesla |
4.2.11. | Tesla's Unusual Approach |
4.2.12. | Tesla's Sensor Suite |
4.2.13. | Super Cruise (GM) and BlueCruise (Ford) |
4.2.14. | Cadillac Escalade - Sensor suite |
4.2.15. | China - XPeng and Arcfox |
4.2.16. | Leaders |
4.2.17. | Private Vehicle Leaders |
4.2.18. | Sensors for Private Vehicles |
4.2.19. | Front Radar Applications |
4.2.20. | The Role of Side Radars |
4.2.21. | Front and Side Radars per Car |
4.2.22. | Total Radars per Car for Different SAE levels |
4.2.23. | Vehicle camera applications |
4.2.24. | E-mirrors, an emerging camera application |
4.2.25. | External Cameras for Autonomous Driving |
4.2.26. | Internal Cameras for Autonomous Driver Monitoring |
4.2.27. | LiDARs in automotive applications |
4.2.28. | LiDAR Deployment |
4.2.29. | Total Sensors For Level 0 to Level 4 and Robotaxis |
4.2.30. | Summary of Privately Owned Autonomous Vehicles |
5. | ROBOTAXIS AND MOBILITY AS A SERVICE (MAAS) |
5.1. | Introduction |
5.1.1. | MaaS Level 4 is Different From Privately Owned Level 4 |
5.1.2. | Robotaxis & Robot Shuttles |
5.2. | California Testing Analysis |
5.2.1. | Key conclusions from California testing |
5.2.2. | The Importance Of California DMV |
5.2.3. | Testing Mileage |
5.2.4. | Furthest testers in 2022 |
5.2.5. | Miles per disengagement |
5.2.6. | Who are the top three? |
5.2.7. | Predicting next years performance |
5.2.8. | Caveats of Measuring Performance With MPD |
5.2.9. | Miles per disengagements - Waymo vs. Cruise |
5.2.10. | How many miles per disengagement is enough? |
5.2.11. | Deeper Look at Cruise's disengagements |
5.2.12. | Could Robotaxis be Good Enough Already |
5.2.13. | Raw Data vs. Adjustments |
5.2.14. | Cruise's Collisions During Testing |
5.2.15. | Driverless Testing Timeline |
5.2.16. | Driver Out Testing - Disengagements and Collisions |
5.2.17. | No. Cars Registered For Driver Out Testing |
5.2.18. | Robotaxi Driverless Crash Rate Compared to San Francisco and US |
5.2.19. | Waymo entering San Francisco |
5.2.20. | Very few collisions are the fault of the autonomous system |
5.2.21. | Nature of Collisions Where Autonomous System Was at Fault (1) |
5.2.22. | Nature of Collisions Where Autonomous System Was at Fault (2) |
5.3. | China disengagement data and commercial deployment |
5.3.1. | Beijing as a parallel to California |
5.3.2. | Top players by miles tested |
5.3.3. | Other companies testing in Beijing |
5.3.4. | Baidu's testing compared to California leaders |
5.3.5. | Fleet size of Baidu compared to Waymo and Cruise |
5.3.6. | Baidu and LuoBoYunLi |
5.4. | Robotaxis In Europe, Japan and ROW. |
5.4.1. | Summary |
5.4.2. | The UK and Oxa (previously Oxbotica) |
5.4.3. | Non-robotaxi in the UK |
5.4.4. | Non-robotaxis in Europe |
5.4.5. | Mobileye in Germany |
5.4.6. | Heavy-Duty Autonomous Vehicles: 2023-2043 |
5.5. | Key Player Analysis |
5.5.1. | Table of Players (1) |
5.5.2. | Table of Players (2) |
5.5.3. | Uber and Lyft starting to struggle in San Fran |
5.5.4. | Driving Sharing Companies and Their Autonomous Partnerships. |
5.5.5. | State of development |
5.5.6. | Robotaxi investment |
5.5.7. | Best Funded Companies in Autonomy and Mobility Space. |
5.5.8. | Waymo |
5.5.9. | Waymo Sensor Suite |
5.5.10. | Cruise |
5.5.11. | Cruise Sensor Suite. |
5.5.12. | Waymo and Cruise's Ground Up Robotaxi Vehicles |
5.5.13. | AutoX |
5.5.14. | AutoX Sensor Suite |
5.5.15. | Baidu/Apollo |
5.5.16. | Baidu's Ground Up Robotaxi |
5.5.17. | Mobileye - One of the Most Significant Testers Not in California |
5.5.18. | Robotaxi Sensor Suite Analysis (1) |
5.5.19. | Robotaxi Sensor Suite Analysis (2) |
5.5.20. | Robotaxi Testing and Deployment Locations (1) |
5.5.21. | Level 4 or level 5? |
6. | ENABLING TECHNOLOGIES: LIDAR, RADAR, CAMERAS, INFRARED, HD MAPPING, TELEOPERATION, 5G AND V2X |
6.1. | Introduction |
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. | Autonomous driving technologies |
6.2.2. | The Sensor Trifactor |
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 |
6.3. | Recommended Sensor Suites For SAE Level 2 to Level 4 & Robotaxi |
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 Robotaxi: The Trifactor |
6.3.9. | Level 4 Robotaxi: Extras |
6.4. | Cameras |
6.4.1. | RGB/Visible light camera |
6.4.2. | Vehicle camera applications |
6.4.3. | Components of a CMOS image sensor die |
6.4.4. | Image sensor bare die |
6.4.5. | E-mirrors, an emerging camera application |
6.4.6. | In-cabin monitoring, an autonomous necessity |
6.4.7. | Performance and application trends |
6.4.8. | Performance attribute priorities |
6.4.9. | The importance of HDR in automotive(1) |
6.4.10. | The importance of HDR in automotive (2) |
6.4.11. | Automotive HDR Compared to Other Technologies |
6.4.12. | How Automotive HDR is Achieved |
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 |
6.5. | IR Cameras |
6.5.1. | IR Cameras |
6.5.2. | Infrared cameras for automotive applications |
6.5.3. | The Need for NIR |
6.5.4. | SWIR for autonomous mobility |
6.5.5. | Other SWIR Benefits: Better On-Road Hazard Detection |
6.5.6. | NIR cameras for automotive applications |
6.5.7. | SWIR Sensitivity of Materials |
6.5.8. | SWIR Imaging: Incumbent and Emerging Technology Options |
6.5.9. | The Challenge of High Resolution, Low Cost IR Sensors |
6.5.10. | Silicon Based SWIR Detection - TriEye. |
6.6. | Radar |
6.6.1. | Radar |
6.6.2. | Automotive Radar |
6.6.3. | Front Radar Applications |
6.6.4. | Side Radars |
6.6.5. | Radar Has a Key Place in Automotive Sensors |
6.6.6. | Radars Limited Resolution |
6.6.7. | Radar Trilemma |
6.6.8. | Radar Anatomy |
6.6.9. | Automotive Radars: Frequency Trends |
6.6.10. | Trends in Transceivers |
6.6.11. | Radar Board Trends |
6.6.12. | Leading players - tier 1 suppliers |
6.6.13. | Transceiver suppliers |
6.6.14. | Radar Trends: Volume and Footprint |
6.6.15. | Radar Trends: Packaging and Performance |
6.6.16. | Radar Trends: Increasing Range |
6.6.17. | Radar Trends: Field of View |
6.6.18. | Radar Trends: Angular Resolution (lower is better) |
6.6.19. | Radar Trends: Virtual Channel Count |
6.6.20. | Two Approaches to Larger Channel Counts |
6.7. | LiDAR |
6.7.1. | LiDARs in automotive applications |
6.7.2. | SWOT analysis of automotive lidar |
6.7.3. | Automotive lidar players by technology |
6.7.4. | Automotive lidar supply chain |
6.7.5. | Cost reduction approaches |
6.7.6. | BOM cost estimation |
6.7.7. | Price/cost composition |
6.7.8. | Lidar price analysis |
6.7.9. | Forecast of lidar unit price by technology 1 |
6.7.10. | Forecast of lidar unit price by technology 2 |
6.7.11. | Existing and near-future passenger vehicles equipped with lidars |
6.7.12. | Autonomous driving levels |
6.7.13. | Radar or lidar |
6.7.14. | Laser range finder function for the first production car |
6.7.15. | Lidar integration positions for ADAS/AV |
6.7.16. | Examples of lidar integration locations |
6.7.17. | Lidar integration in lamps |
6.7.18. | Lidar integration in the grille |
6.7.19. | Lidar integration on/in the roof |
6.7.20. | Lidars integrated in other positions |
6.7.21. | Lidar certification process |
6.7.22. | Other commercialized vehicles equipped with Lidar |
6.8. | Mapping and Localisation |
6.8.1. | What is Localisation? |
6.8.2. | Localization: Absolute vs Relative |
6.8.3. | Lane Models: Uses and Shortcomings |
6.8.4. | HD Mapping Assets: From ADAS Map to Full Maps for Level-5 Autonomy |
6.8.5. | Many Layers of an HD Map for Autonomous Driving |
6.8.6. | HD Map as a Service |
6.8.7. | Who are the Players? |
6.8.8. | Mapping Business Models |
6.8.9. | Vertically Integrated Mappers |
6.8.10. | HD Mapping with Cameras |
6.8.11. | HD Mapping with Cameras |
6.8.12. | DeepMap |
6.8.13. | Civil Maps |
6.8.14. | Semi- or Fully Automating the Data-to-Map Process |
6.8.15. | Radar Mapping |
6.8.16. | Radar Localisation: Navtech |
6.8.17. | Radar Localisation: WaveSense |
6.9. | Teleoperation |
6.9.1. | Enabling Autonomous MaaS |
6.9.2. | 3 Levels of Teleoperation |
6.9.3. | How remote assistance works - Zoox |
6.9.4. | Remote assistance |
6.9.5. | Remote Control |
6.9.6. | Where is teleoperation currently used? |
6.9.7. | Players |
6.9.8. | MaaS vs Independent solution providers |
6.9.9. | Ottopia's Advanced Teleoperation (1) |
6.9.10. | Ottopia's Advanced Teleoperation (2) |
6.9.11. | Phantom Auto's Teleoperation as Back-Up for AVs |
6.9.12. | Phantom Auto Gaining Momentum in Logistics |
6.9.13. | Halo - Subverting Autonomy |
6.10. | Connectivity: WiFi, 5G, 6G, LiFi |
6.10.1. | Vehicle-to-Everything (V2X) |
6.10.2. | Why V2X Matters for Autonomy |
6.10.3. | Wi-Fi vs Cellular |
6.10.4. | Why V2X Matters for Autonomy |
6.10.5. | Comparison of Wi-Fi and Cellular based V2X |
6.10.6. | Regulatory: Wi-Fi based vs Cellular V2X |
6.10.7. | Standards for Communication |
6.10.8. | V2X Technologies Across the World |
6.10.9. | OEM Applications of Connected Technologies |
6.10.10. | Use Cases and Applications of Cellular V2X Overview |
6.10.11. | Cellular V2X for Automated Driving Use Case (1) |
6.10.12. | Use Cases of 5G NR Cellular V2X for Autonomous Driving |
6.10.13. | Cellular V2X for Automated Driving Use Case |
6.10.14. | Case study: 5G to Provide Comprehensive View for Autonomous Driving |
6.10.15. | Ford Cellular V2X from 2022 |
6.10.16. | Landscape of Supply Chain |
6.10.17. | 6G - The Next Generation of Communications |
7. | FORECASTS |
7.1. | Forecasting Methodology: Robotaxis |
7.2. | Robotaxi Commercial Service market entry by region |
7.3. | Robotaxi Testing and Services 2016-2022 |
7.4. | Commercial Service Rollout 2024-2044 |
7.5. | Robotaxi Fleet Size 2024-2044 |
7.6. | Robotaxi Service Utilization and Adoption |
7.7. | Robotaxi Service Revenue 2024-2044 |
7.8. | Private and Autonomous Passenger Vehicle Mileage 2022-2044 |
7.9. | Forecasting Methodology: Private Cars (1) |
7.10. | Forecasting Methodology: Private Cars (2) |
7.11. | Global Vehicle Sales and Peak Car by Region 2019-2044 |
7.12. | Forecasting Methodology: Progression of Level 0, Level 1 and Level 2 |
7.13. | Forecasting Methodology: Emergence of level 3 and Level 4 Technologies |
7.14. | Global Vehicle Sales and Peak Car by SAE Level 2022-2044 |
7.15. | Autonomous Vehicle Adoption in US 2022-2044 |
7.16. | Autonomous Vehicle Adoption in China 2022-2044 |
7.17. | Autonomous Vehicle Adoption in EU + UK + EFTA 2022-2044 |
7.18. | Autonomous Vehicle Adoption in Japan 2022-2044 |
7.19. | Autonomous Vehicle Adoption in ROW 2022-2044 |
7.20. | Forecasting Method: Vehicle Revenue |
7.21. | Automotive Market Revenue by Region 2022-2044 |
7.22. | Automotive Market Revenue by SAE Level 2022-2044 |
7.23. | Forecasting Method: Sensors |
7.24. | Sensors for Cars: Cameras |
7.25. | Sensors for Cars: Radar |
7.26. | Sensors for Cars: LiDAR |
7.27. | Sensors for Cars Revenue: 2022-2044 |
8. | COMPANY PROFILES |
8.1. | OEM/Robotaxi Company |
8.1.1. | Zoox |
8.1.2. | Waymo |
8.1.3. | Xpeng |
8.1.4. | Stellantis |
8.1.5. | MOIA |
8.1.6. | Nuro |
8.2. | Tier 1 Supplier |
8.2.1. | Bosch |
8.2.2. | Valeo |
8.2.3. | Continental (radar and LiDAR) |
8.3. | Other Supplier |
8.3.1. | AMS Osram |
8.3.2. | Qualcomm |
8.3.3. | Mobileye |
8.3.4. | NXP |
8.3.5. | Kognic |
8.4. | Cameras and thermal cameras |
8.4.1. | Nodar |
8.4.2. | Owl |
8.4.3. | TriEye |
8.5. | Radar |
8.5.1. | Pontosense |
8.5.2. | Plastic Omnium |
8.5.3. | Metawave |
8.5.4. | Uhnder |
8.5.5. | Smart Radar System |
8.5.6. | Zendar |
8.5.7. | Spartan Radar |
8.5.8. | Zadar Labs |
8.6. | LiDAR |
8.6.1. | PreAct |
8.6.2. | RoboSense |
8.6.3. | AEye |
8.6.4. | Cepton |
8.6.5. | Auto L |
8.6.6. | Vueron |
8.7. | Connected Infrastructure |
8.7.1. | Continental (Connect Infrastructure) |
8.7.2. | Derq |