| 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 |