新兴 SAE 3 级汽车和自动驾驶 MaaS 驱动汽车传感器市场 10 年 13% 的年均复合增长率
自动驾驶汽车、机器人出租车和传感器 2024-2044
汽车销售高峰、商用机器人出租车服务部署、ADAS 功能和采用、SAE 3 级认证车辆的出现、移动即服务 (MaaS)、激光雷达、雷达、摄像头、车内摄像头、热成像、高清地图、5G 和 V2X、市场预测
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自动驾驶汽车的障碍终于消失。私人 SAE 3 级车辆现已向公众开放，多种商用机器人出租车服务也已推出。了解哪些地方可以使用 3 级车辆，哪些城市有机器人出租车服务，这些车辆需要哪些传感器（摄像头、雷达和激光雷达），以及机器人出租车服务如何在 20 年内创造超过 1 万亿美元的年收入。
It's an exciting time for the autonomous and automated vehicle industry with the maturity of next generation technologies evolving rapidly and driverless services coming of age. This report provides a clear understanding of what technologies are available on the market, how they have been adopted so far, and how they impact downstream markets like sensors.
After years of waiting, false starts, and much hope, a new era has arrived for the automotive industry. The age of autonomous cars is beginning to dawn and the next 20-years, forecasted in this report, will be transformative for the transportation industry.
Emergence of level 3 vehicles
The automotive industry has been stuck at SAE level 2 for some time. Consumers have had access to technologies like adaptive cruise control, and lane keep assistance systems, but they have so far always been responsible for the vehicle. Some hands-free systems have come to market, such as GM's "Super Cruise" and Ford's "Blue Cruise", but level 3 has been a long time coming. Audi attempted level 3 in 2017 with the A8 that came equipped with cameras, radars and LiDAR, but it was before its time and was limited by regulators to level 2. Honda successfully certified the Legend for level 3 use in Japan in 2021 after some important legislative changes, but only made 100 units available. It is Mercedes and the S-Class that has made some serious ground with level 3, achieving certification in Germany, and parts of the US. Time and again Mercedes has showcased automotive technologies on the S-Class that eventually trickle down to the rest of the automotive market.
Understanding how Mercedes have reached this milestone, what regulatory changes have facilitated level 3 vehicles and who else is working towards level 3 is critical for understanding the outlook for automated technologies in the car market. Furthermore, IDTechEx provides understanding of long-term automotive market trends that govern how long it will take for level 3 technologies to become mainstream and when the industry can expect level 4 technologies to reach the consumer automotive market.
The continued adoption of ADAS
Away from flagship vehicles and the cutting edge, IDTechEx has also measured an increase in the adoption of ADAS (advanced driver assistance systems) features like adaptive cruise control, automatic emergency braking and lane keep assistance systems. Safety is a primary driver for the adoption of these features, particularly automatic emergency braking which contributes towards increased safety for vulnerable road users, and lane departure warning systems which can help avoid collisions on motorways. These systems are enabled with sensors like cameras, automotive radar and automotive LiDAR. Once these systems and sensors are on the vehicle it becomes easy for OEMs to start providing the convenience features, such as adaptive cruise control and high-way pilot assist, creating additional value for OEMs
IDTechEx has built a database of more than 150 of the best-selling cars from around the world in 2022, representing 29% of the total car market. Each vehicle's features and optional extras are evaluated leading to a comprehensive picture in this report of key ADAS feature adoption and sensor requirements for the automotive industry today. Furthermore, IDTechEx has multiple years of this data, combined with analysis of over 4,000 vehicle brochures to provide long-term understanding of adoption rates in the automotive industry, guiding the forecasts in this report.
It is the prevalence and growth ADAS that is have the biggest impact on the automotive sensor market today, requiring multiple cameras and radar per vehicle. Sensor suites will continue to grow as new features become available and the industry further deploys SAE level 3, begins developing SAE level 4, and as robotaxis that can carry up to 40 sensors begin taking off. This report explains exactly what sensors are needed for each SAE level, how many sensors are going into the market at the moment, what the outlook for the automotive sensor market is, and how it will flourish with increasing autonomous technology adoption.
Robotaxis and Autonomous MaaS
In addition to progress in the private vehicle market, the robotaxi market is finally starting to enter its nascent stages. Commercial services have theoretically been available since 2017, but early examples were limited to early access groups and not general public. In 2020 this changed when Waymo started letting members of the public use its completely driverless ride hailing service in Phoenix, Arizona. In 2022 driverless ride hailing services have opened to the public in multiple cities across the US and China including San Francisco, Las Vegas, Beijing, Shenzhen and more. In 2023 Companies like Alphabet's Waymo, General Motors' Cruise, Baidu's Apollo and more are spreading to more cities and increasing their operational design domain at their existing locations.
The growth and expansion of robotaxi services is highly dependent on autonomous vehicles being able to demonstrate safety. This is not easy to measure, this report provides deep analysis into robotaxi safety using data from over 450 crash reports involving autonomous test vehicles in California as well over 15 million miles worth of on road testing. In the past this data has shown that autonomous vehicle safety is improving at an exponential rate, in 2023 this analysis shows that autonomous vehicles are going to be close to human levels of safety.
Key aspects
Private autonomous vehicles from SAE level 0 to SAE level 4
Uptake of ADAS features and sensors on private cars, adoption percentages from vehicles sold in 2020 and 2022 for
- Cruise control
- Adaptive cruise control
- Automatic emergency braking
- Blind spot detection and monitoring
- Lane keep assistance systems
- Camera
- Radar
- LiDAR
Robotaxis and emerging robotaxi services, and analysis of robotaxi autonomous safety performance
Market forecasts for the US, China, EU + UK + EFTA, Japan, and ROW across SAE level 0 to SAE level 4 and robotaxis.
| Report Metrics | Details |
|---|---|
| Historic Data | 2019 - 2023 |
| CAGR | Emerging SAE level 3 cars and autonomous MaaS drive 10-year CAGR of 13% in automotive sensor markets |
| Forecast Period | 2024 - 2044 |
| Forecast Units | Unit sales, revenue (US$) |
| Regions Covered | Europe, United States, China, Japan, Worldwide |
| Segments Covered | Private SAE level 0 Private SAE level 1 Private SAE level 2 Private SAE level 3 Private SAE level 4 Level 4 robotaxi |
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| 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 |
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自动驾驶汽车、机器人出租车和传感器 2024-2044
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报告统计信息
| 幻灯片 | 353 |
|---|---|
| 预测 | 2044 |
| ISBN | 9781915514813 |
预览内容
 Webinar Slides - EOY 2023
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Webinar Slides
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Sample pages
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