2024年-2044年无人驾驶巴士和自动驾驶汽车：技术、趋势、预测
无人驾驶巴士和自动驾驶汽车 - 美国，欧盟和中国市场、主要参与者、趋势、安全、法规、行业分析、传感器、移动即服务(MaaS)、赋能技术和市场预测
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本报告深入探讨了无人驾驶巴士与自动驾驶汽车的市场现状、技术革新及主要参与者。报告详尽分析了来自15个国家的20家参与者，展现了这些企业在市场活动中的表现与策略。报告基于自2019年以来的历史销售数据，以中国、欧洲、美国及世界其他地区（RoW）为地域划分，对2024年至2044年的市场进行了全面预测，揭示了无人驾驶巴士和自动驾驶汽车的广阔前景。到2044年，无人驾驶巴士和自动驾驶汽车市场规模预计将增长到670亿美元以上。
这份关于机器人穿梭车和自动驾驶巴士的报告详细分析了该行业的参与者和活动。通过2019年以来的历史销售数据对当前市场进行了背景分析，并涵盖了中国、欧洲、美国和其他地区的区域数据。报告中确定了行业的主要挑战和机遇，并预测了其商业部署和区域政策。每个市场的高保真分析为IDTechEx的20年预测提供了指导。
本报告的主要内容包括
- 机器人穿梭车和自动驾驶巴士行业概述
- 各企业的产品、商业化和活动概述
- 机器人穿梭车和自动驾驶巴士的发展趋势和性能分析
- 应用技术概述，包括摄像头、热像仪、量子点作为红外、近红外、短波红外、激光雷达和雷达的光学传感器材料。
- 对 20 年内机器人穿梭车和自动驾驶巴士的销售和收入进行详细预测
- 内容摘要，涵盖影响机器人穿梭车和自动驾驶巴士行业的主要趋势和主要预测。
- 机器人穿梭车： 参与者与分析
o 机器人穿梭车的主要启示
o 自动驾驶监管机构分析--欧盟、美国和中国
o 参与者名单--机器人穿梭车初创企业
o 自动驾驶卡车的冗余性
o 机器人穿梭车分析--价值链中的参与者
o 机器人穿梭车分析--总拥有成本（TCO）分析
o 机器人穿梭车分析--机器人穿梭车成功/失败的原因
- 自动驾驶巴士： 参与者和分析
o 巴士类别
o 自动驾驶服务类型
o 自动驾驶面临的挑战
o 参与者名单--小型巴士、中型巴士、城市巴士
o 自动驾驶巴士分析--活跃的自动驾驶巴士公司数量
o 自动驾驶巴士分析--自动驾驶巴士成功/失败的原因
- 赋能技术： 摄像头
o CMOS分类、工作原理、操作过程、扫描类别和要求
- 应用技术：热像仪
o 热像仪 SWOT分析
o 高分辨率、低成本红外传感器的挑战
- 赋能技术： 量子点作为红外、近红外、短波红外光学传感器材料
o 红外、近红外、短波红外的分类、工作原理、操作过程和要求
- 赋能技术： 激光雷达
o 激光雷达的分类、工作原理、操作流程和要求
o 激光雷达 SWOT分析
- 赋能技术： 雷达
o 雷达 SWOT分析
o 雷达关键部件、4D 雷达和成像雷达
- 预测：机器人穿梭车
当前和预测的 2020-2044 年城市推广情况
o 不同经济体的机器人穿梭巴士票价
2020-2044 年机器人穿梭车单位销量
2022-2044 年机器人穿梭车收入、车辆销售和乘客票价
2020-2044 年机器人穿梭车的传感器
- 预测： 自动驾驶巴士
小型巴士的使用、采用和城市推广
2022-2044 年自动驾驶巴士销量
车辆定价 2022-2044 年
2022-2044 年自动驾驶巴士收入
o 自动驾驶巴士和自动驾驶巴士的座位数
2023-2044 年自自动驾驶巴士的动力系统
o 自动驾驶巴士的传感器 2024-2044 年
- 预测：机器人穿梭车和自动驾驶巴士比较
o 自动驾驶巴士和机器人穿梭车的座位数
2022-2044 年机器人穿梭车和自动驾驶巴士单位销量
o 2022-2044 年机器人穿梭车和自动驾驶巴士的销售收入
o 2024-2044 年机器人穿梭车和自动驾驶巴士的传感器
In recent years promise of a public transport revolution is being teased by autonomous buses and roboshuttles. These technologies promise to deliver significant cost reductions for operators and alleviate labor pressures. Although full commercialization remains some distance away, advancements in this sector hold substantial potential for addressing many current industry challenges. This report provides a comprehensive analysis of the roboshuttles and autonomous buses industry, highlighting critical challenges, market dynamics, and future outlook.
Critical Challenges in the Roboshuttles and Autonomous Buses Industry
The development of urban public transportation faces several significant challenges. The rising average age of urban populations exacerbates labor shortages, making it increasingly difficult to find enough drivers to meet demand. This issue is further complicated by the rapid pace of urban development, which creates new challenges for efficient and effective public transportation. Additionally, the continuous improvement of urban infrastructure demands innovative solutions that can adapt to evolving needs. Autonomous buses and roboshuttles offer promising solutions to these problems by potentially replacing drivers in all bus use-cases, supporting drivers in conventional buses, and providing fully automated services in specific operational design domains such as airports.
Replacing drivers with autonomous systems can significantly lower operational costs. Driver salaries constitute a considerable portion of the operational costs for any commercial vehicle, and autonomous technology can offer large potential savings in this area. Furthermore, autonomous technology promises to drastically improve safety by reducing the number of traffic accidents. Human error accounts for 90-95% of all incidents, and autonomy offers a future where traffic accidents are significantly reduced. Cost savings from autonomous technology could also make it feasible to serve previously unprofitable routes, improving mobility in underserved areas such as small villages.
Industry Dynamics and Market Shifts
The roboshuttles market has seen significant shifts in recent years. From 2020 to 2024, the number of players in the market halved, reflecting the typical lifecycle of emerging industry bubbles, where the transition from innovation to commercial viability presents significant challenges. Despite this decrease in player numbers, 2023 saw notable activity in the roboshuttles sector. European leader Navya was acquired and rebranded by a Japanese company, while other financially strong players such as Toyota and Cruise exited the market. Meanwhile, Asian companies like WeRide, QCraft, and PIX Moving are rapidly expanding, demonstrating the dynamic and evolving nature of the industry.
IDTechEx believes that progress in the autonomous buses sector has been slower due to limited commercial scenarios and regulatory challenges. The higher requirements for infrastructure and lack of specific regulations for autonomous buses hinder widespread adoption. Currently, the lack of global regulations defining testing scope and procedures for autonomous buses impacts their deployment. Additionally, the complex environments in which autonomous buses operate, such as high-speed roads, multiple passengers, and intricate urban settings, pose further challenges. As a result, many companies have focused on testing and commercialization in highly controlled environments, such as closed campuses or predefined routes.
Comprehensive Analysis and Future Outlook
This report provides an in-depth analysis of the roboshuttles and autonomous buses industry, including policy support and future forecasts for China, the U.S., and Europe. IDTechEx's research covers a 20-year forecast period, offering detailed market predictions and trends. The report highlights the capabilities of Chinese startups such as WeRide, QCraft, and PIX Moving, and examines different drivetrain configurations. IDTechEx also estimated the manufacturing costs of autonomous shuttles and buses in different markets, revealing a cost disparity of over 3 times. This comprehensive analysis provides valuable insights into the most suitable vehicle capacities and constructive suggestions for industry development.
This report on Roboshuttles and Autonomous Buses provides a detailed analysis of the players and activities within the sector. Current market is contextualised through historical data on sales back to 2019, with regional granularity across China, Europe, USA and RoW. Key challenges and opportunities are identified for the industry, with predictions regarding their commercial deployment and Regional policies. The high-fidelity analysis of each market guides IDTechEx's 20-year forecasts.
Key aspects of this report include:
- An overview of the Roboshuttles and Autonomous Buses industry
- A summary of each player's product, commercialization, and activity
- Roboshuttles and Autonomous Buses trends and performance analysis
- An overview of Enabling Technologies include Cameras, Thermal Cameras, Quantum Dots as Optical Sensor Materials for IR, NIR, SWIR, LiDAR, and Radar.
- Granular 20-year forecasts for Roboshuttles and Autonomous Buses sales and revenue
| Report Metrics | Details |
|---|---|
| Historic Data | 2019 - 2023 |
| CAGR | The global Roboshuttles and Autonomous Buses market will grow at a CAGR of 38.9% between 2034 and 2044 - reaching nearly half a million sales annually. |
| Forecast Period | 2024 - 2044 |
| Forecast Units | units, US$ |
| Regions Covered | Worldwide, United States, China, Europe |
| Segments Covered | Roboshuttles, sensors, transport as a service, autonomous buses, electric buses, |
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| 1. | EXECUTIVE SUMMARY |
| 1.1. | Roboshuttles and Autonomous Buses 2024-2044 |
| 1.2. | What makes it a roboshuttle? |
| 1.3. | Distribution of roboshuttle cities |
| 1.4. | Autonomous bus introduction |
| 1.5. | Categories of bus |
| 1.6. | Technology Readiness |
| 1.7. | Different powertrains for different vehicles |
| 1.8. | Types of service for roboshuttles and buses |
| 1.9. | Number of active companies |
| 1.10. | The Sensor Trio |
| 1.11. | Sensor suites for Roboshuttles and autonomous buses |
| 1.12. | SWOT analysis and comparisons for roboshuttles and autonomous buses |
| 1.13. | Commercial readiness and opportunity comparison of roboshuttles and autonomous buses |
| 1.14. | IDTechEx predicted timelines |
| 1.15. | Roboshuttle and unit sales 2020-2044 |
| 1.16. | Roboshuttle revenues, vehicle sales and passenger fares 2022-2044 |
| 1.17. | Roboshuttle revenues, vehicle sales and passenger fares 2022-2044 |
| 1.18. | Autonomous bus unit sales 2022-2044 |
| 1.19. | Autonomous bus unit sales by regions 2022-2044 |
| 1.20. | Autonomous bus revenue 2022-2044 |
| 1.21. | Autonomous bus revenue by region 2022-2044 |
| 1.22. | Roboshuttle and autonomous bus sales revenue 2022-2044 |
| 1.23. | Access more with an IDTechEx Subscription |
| 2. | ROBOSHUTTLES: PLAYERS AND ANALYSIS |
| 2.1. | Introduction |
| 2.1.1. | Key Takeaways For Roboshuttles |
| 2.1.2. | What Makes it a Roboshuttle? - Part 1 |
| 2.1.3. | What Makes it a Roboshuttle? - Part 2 |
| 2.1.4. | Table Comparison Of Active Companies |
| 2.1.5. | EasyMile |
| 2.1.6. | EasyMile Real World Trials And Testing |
| 2.1.7. | HOLON |
| 2.1.8. | Auve Tech |
| 2.1.9. | GAMA (Formerly Navya) |
| 2.1.10. | GAMA Use Case Examples |
| 2.1.11. | GAMA (Formerly Navya)'s Business Model |
| 2.1.12. | Zoox |
| 2.1.13. | Zoox Sensor Suite |
| 2.1.14. | PIX Moving |
| 2.1.15. | Yutong and WeRide |
| 2.1.16. | Yutong Use Cases accelerate by WeRide.ai |
| 2.1.17. | Qcraft |
| 2.1.18. | Apollo - Autonomous Branch of Baidu |
| 2.1.19. | Ohmio - Lift |
| 2.1.20. | Ohmio Trials |
| 2.1.21. | Lohr, Torc and Transdev |
| 2.1.22. | Beep -Olli 2.0 |
| 2.2. | Roboshuttle projects that have become dormant |
| 2.2.1. | Table Comparison Of Inactive Companies |
| 2.2.2. | ZF - A Robot Shuttle Future. |
| 2.2.3. | ZF - Robot Shuttle Deployment (Rivium3.0) |
| 2.2.4. | ZF with authorized service providers and manufacturers |
| 2.2.5. | ZF - Strategic Realignment 2030 |
| 2.2.6. | Toyota e-PALETTE |
| 2.2.7. | Cruise Origin |
| 2.3. | Roboshuttle projects that have been discontinued |
| 2.3.1. | Table Comparison of Discontinued Companies |
| 2.3.2. | NEVS |
| 2.3.3. | May Mobility |
| 2.3.4. | Higer |
| 2.3.5. | Coast |
| 2.3.6. | Sensible 4 - GACHA |
| 2.3.7. | IAV and the HEAT project |
| 2.3.8. | Continental |
| 2.3.9. | Bosch |
| 2.3.10. | Local Motors - Olli |
| 2.3.11. | e.Go Moove |
| 2.3.12. | DGWORLD |
| 2.3.13. | Projects That Are No Longer Active (1) |
| 2.3.14. | Projects That Are No Longer Active (2) |
| 2.3.15. | Projects That Are No Longer Active (3) |
| 2.4. | Roboshuttles analysis and conclusions |
| 2.4.1. | Table Comparison Of Active Companies |
| 2.4.2. | Technology Readiness before 2023 |
| 2.4.3. | Technology Readiness - Still Active in 2024 |
| 2.4.4. | Decline in Roboshuttle Companies (1) |
| 2.4.5. | Decline in Roboshuttle Companies (2) |
| 2.4.6. | Where Players Exit |
| 2.4.7. | Where Are Players In The Value Chain (1) |
| 2.4.8. | Where Are Players In The Value Chain (2) |
| 2.4.9. | Passenger Capacity |
| 2.4.10. | Total Cost of Ownership Analysis (1) |
| 2.4.11. | Total Cost of Ownership Analysis (2) |
| 2.4.12. | Reasons Roboshuttles Will Succeed (1) |
| 2.4.13. | Reasons Roboshuttles Will Succeed (2) |
| 2.4.14. | Reasons Roboshuttles Will Succeed (3) |
| 2.4.15. | Reasons Roboshuttles Will Fail (1) |
| 2.4.16. | IDTechEx Opinion On Roboshuttles |
| 3. | AUTONOMOUS BUSES: PLAYERS AND ANALYSIS |
| 3.1. | Introduction |
| 3.1.1. | Categories of Bus |
| 3.1.2. | Bus Category Sizing |
| 3.1.3. | Reasons to automate |
| 3.1.4. | Types of Autonomous Services |
| 3.1.5. | Challenges of Automating |
| 3.1.6. | Table Comparison Of Active Players (1) |
| 3.1.7. | Table Comparison Of Active Players (2) |
| 3.2. | Players - Minibuses |
| 3.2.1. | eVersum |
| 3.2.2. | King Long |
| 3.2.3. | BrightDrive |
| 3.2.4. | Aurrigo |
| 3.2.5. | Hyundai Autonomous Bus |
| 3.2.6. | Volkswagen |
| 3.2.7. | Volkswagen ID.Buzz - Sensor Suite |
| 3.2.8. | Volkswagens MOIA Project |
| 3.2.9. | Perrone Robotics - Overview |
| 3.2.10. | Perrone Robotics - Sensor Suite |
| 3.2.11. | Perrone Robotics - Deployment And Planned Rollout |
| 3.3. | Players - Midibuses |
| 3.3.1. | eVersum |
| 3.3.2. | ADASTEC |
| 3.3.3. | ADASTEC and Karsan - Sensor Suite |
| 3.3.4. | ADASTEC Trial deployments |
| 3.3.5. | Golden Dragon ASTAR |
| 3.3.6. | QCraft |
| 3.3.7. | QCraft - Sensor Suite |
| 3.3.8. | Zhongtong |
| 3.4. | Players - City Buses |
| 3.4.1. | Hyundai Autonomous Bus |
| 3.4.2. | Fusion Processing - Overview |
| 3.4.3. | Fusion Processing - Testing and Trials |
| 3.4.4. | ANA and BYD - Airport Bus Trials |
| 3.4.5. | New Flyer - Overview |
| 3.4.6. | New Flyer - Sensor Suite |
| 3.4.7. | DeepBlue |
| 3.4.8. | DeepBlue Trials |
| 3.5. | Projects that have become dormant |
| 3.5.1. | LILEE |
| 3.5.2. | Irizar |
| 3.5.3. | Iveco |
| 3.6. | Companies No Longer Active In Autonomous Buses |
| 3.6.1. | ST Engineering |
| 3.6.2. | Daimler |
| 3.6.3. | Scania |
| 3.6.4. | Proterra |
| 3.6.5. | Other Big Players Either Not Involved Or Stopped |
| 3.7. | Autonomous Bus Analysis |
| 3.7.1. | Bus Sizes |
| 3.7.2. | Activity |
| 3.7.3. | Technology Readiness |
| 3.7.4. | Few Large Trials |
| 3.7.5. | Table Comparison Of Active Players (1) |
| 3.7.6. | Table Comparison Of Active Players (2) |
| 3.7.7. | Vehicle Type vs Company Type |
| 3.7.8. | Companies in Value Chain |
| 3.7.9. | Options For Early Deployments Of Autonomous Tech |
| 3.7.10. | Autonomous Bus Deployments in other ODDs |
| 3.7.11. | Drivetrains - Most Are Thinking Electric |
| 3.7.12. | Reasons Autonomous Buses Will Be A Success |
| 3.7.13. | Reasons Autonomous Buses Will Fail |
| 3.7.14. | IDTechEx Opinion On Autonomous Buses |
| 4. | ENABLING TECHNOLOGIES: CAMERAS |
| 4.1. | Cameras in Roboshuttles and Autonomous buses |
| 4.2. | RGB/Visible light camera |
| 4.3. | CMOS image sensors vs CCD cameras |
| 4.4. | Key Components of CMOS |
| 4.5. | Front vs backside illumination |
| 4.6. | Reducing Cross-talk |
| 4.7. | Global vs Rolling Shutter |
| 4.8. | TPSCo: Leading foundry for global shutter |
| 4.9. | Sony: CMOS Breakthrough? |
| 4.10. | Sony: BSI global shutter CMOS with stacked ADC |
| 4.11. | OmniVision: 2.µm global shutter CMOS for automotive |
| 4.12. | Hybrid organic-Si global shutter CMOS |
| 4.13. | Event-based Vision: A New Sensor Type |
| 4.14. | What is Event-based Sensing? |
| 4.15. | General event-based sensing: Pros and cons |
| 4.16. | What is Event-based Vision? (I) |
| 4.17. | What is Event-based Vision? (II) |
| 4.18. | What is event-based vision? (III) |
| 4.19. | What does event-based vision data look like? |
| 4.20. | Event Based Vision in Autonomy? |
| 5. | ENABLING TECHNOLOGIES: THERMAL CAMERAS |
| 5.1. | Thermal Cameras in Roboshuttles and Autonomous buses |
| 5.2. | Segmenting the Electromagnetic Spectrum |
| 5.3. | Thermal camera SWOT |
| 5.4. | The Need for NIR |
| 5.5. | OmniVision: Making Silicon CMOS Sensitive to NIR |
| 5.6. | OmniVision: Making Silicon CMOS Sensitive to NIR |
| 5.7. | Motivation for Short-Wave Infra-Red (SWIR) Imaging |
| 5.8. | Why SWIR in Autonomous Mobility |
| 5.9. | Other SWIR Benefits: Better On-Road Hazard Detection |
| 5.10. | SWIR Sensitivity of Materials |
| 5.11. | SWIR Imaging: Incumbent and Emerging Technology Options |
| 5.12. | The Challenge of High Resolution, Low Cost IR Sensors |
| 5.13. | Silicon Based SWIR Detection - TriEye |
| 6. | ENABLING TECHNOLOGIES: QUANTUM DOTS AS OPTICAL SENSOR MATERIALS FOR IR, NIR, SWIR |
| 6.1. | Quantum Dots as Optical Sensor Materials |
| 6.2. | Quantum Dots: Choice of the Material System |
| 6.3. | Other Ongoing Challenges |
| 6.4. | Advantage of Solution Processing |
| 6.5. | QD-Si CMOS at IR and NIR |
| 6.6. | Hybrid QD-Si Global Shutter CMOS at IR and NIR |
| 6.7. | Emberion: QD-Graphene SWIR Sensor |
| 6.8. | Emberion: QD-Graphene-Si Broadrange SWIR sensor |
| 6.9. | SWIR Vision Sensors: First QD-Si Cameras and/or an Alternative to InVisage? |
| 6.10. | QD-ROIC Si-CMOS integration examples (IMEC) |
| 6.11. | QD-ROIC Si-CMOS Integration Examples (RTI International) |
| 6.12. | QD-ROIC Si-CMOS Integration Examples (ICFO) |
| 7. | ENABLING TECHNOLOGIES: LIDAR |
| 7.1. | LiDAR in Roboshuttles and Autonomous buses |
| 7.2. | LiDAR classifications |
| 7.3. | Automotive LiDAR: Operating process |
| 7.4. | Automotive LiDAR: Requirements |
| 7.5. | LiDAR system |
| 7.6. | LiDAR working principle |
| 7.7. | Laser range finder function for the first production car |
| 7.8. | Comparison of lidar product parameters |
| 7.9. | TOF vs FMCW LiDAR |
| 7.10. | LiDAR scanning categories |
| 7.11. | Summary of lidars with various beam steering technologies |
| 7.12. | Comparison of common beam steering options |
| 7.13. | Overview of beam steering technologies |
| 7.14. | Point cloud |
| 7.15. | Lidar signal applications |
| 7.16. | 3D point cloud modelling |
| 7.17. | LiDAR challenges |
| 7.18. | Poor weather performance: Challenges & solutions |
| 7.19. | Early possible adoption of Lidar |
| 7.20. | Velodyne lidar portfolios |
| 7.21. | Valeo SCALA |
| 7.22. | Livox: Risley prisms |
| 7.23. | Automotive lidar players by technology |
| 8. | ENABLING TECHNOLOGIES: RADAR |
| 8.1. | Radar in Roboshuttles and Autonomous buses |
| 8.2. | Radar SWOT |
| 8.3. | Typical Sensor Suite for Autonomous Cars |
| 8.4. | Radar Has a Key Place in Automotive Sensors |
| 8.5. | The Need for and Emergence of Imaging Radar |
| 8.6. | 4D Radars and Imaging Radars |
| 8.7. | Radar Trends: Volume and Footprint |
| 8.8. | Radar Trends: Packaging and Performance |
| 8.9. | Radar Trends: Increasing Range |
| 8.10. | Radar Trends: Field of View |
| 8.11. | Radar Trilemma |
| 8.12. | Radar Anatomy |
| 8.13. | Radar Key Components |
| 8.14. | Primary Radar Components - The Antenna |
| 8.15. | Primary Radar Components - the RF Transceiver |
| 8.16. | Primary Radar Components - MCU |
| 8.17. | Automotive Radars: Frequency Trends |
| 8.18. | Trends in Transceivers |
| 8.19. | Two Approaches to Larger Channel Counts |
| 8.20. | Semiconductor Technology Trends in Radar |
| 8.21. | Funding for Radar Start-ups |
| 8.22. | Future Radar Packaging Choices |
| 8.23. | Leading players - tier 1 suppliers |
| 8.24. | Transceiver suppliers |
| 8.25. | Supply chain |
| 8.26. | Example products from a tier 1 - Continental |
| 8.27. | Example products from a tier 1 - Bosch |
| 8.28. | Example of radar start-up - Arbe |
| 8.29. | Arbe and its Investors |
| 8.30. | Example of radar start-up - Zadar |
| 9. | FORECASTS |
| 9.1. | Notes on the forecasts chapter |
| 9.2. | Forecasts: Roboshuttles |
| 9.2.1. | Method |
| 9.2.2. | Vehicle assumptions |
| 9.2.3. | Cities Considered |
| 9.2.4. | Adoption within cities |
| 9.2.5. | Current and forecasted city roll out 2020-2044 (1) |
| 9.2.6. | Current and forecasted city roll out 2020-2044 (2) |
| 9.2.7. | Distribution of roboshuttle cities |
| 9.2.8. | Roboshuttle fare pricing for different economies |
| 9.2.9. | Roboshuttle price decline |
| 9.2.10. | Roboshuttle unit sales 2020-2044 |
| 9.2.11. | Roboshuttle revenues, vehicle sales and passenger fares 2022-2044 |
| 9.2.12. | Sensors for roboshuttles 2020-2044 |
| 9.3. | Forecasts: Autonomous Buses |
| 9.3.1. | Method |
| 9.3.2. | Minibus utilization, adoption and city roll-out |
| 9.3.3. | Autonomous bus adoption |
| 9.3.4. | Autonomous bus unit sales 2022-2044 |
| 9.3.5. | Autonomous bus unit sales by regions 2022-2044 |
| 9.3.6. | Vehicle pricing |
| 9.3.7. | Autonomous bus revenue 2022-2044 |
| 9.3.8. | Autonomous bus revenue by region 2022-2044 |
| 9.3.9. | Powertrains of autonomous buses 2023-2044 |
| 9.3.10. | Sensors for autonomous buses 2024-2044 |
| 9.4. | Forecasts: Roboshuttles and Autonomous Buses Comparison |
| 9.4.1. | Seating capacity in autonomous buses and roboshuttles |
| 9.4.2. | Roboshuttle and Autonomous Bus Unit Sales 2022-2044 |
| 9.4.3. | Roboshuttle and Autonomous Bus Sales Revenue 2022-2044 |
| 9.4.4. | Sensors for Roboshuttles and Autonomous Buses 2024-2044 |
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2024年-2044年无人驾驶巴士和自动驾驶汽车：技术、趋势、预测
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到2044年，全球无人驾驶巴士和自动驾驶汽车的销售收入将超过670亿美元。
报告统计信息
| 幻灯片 | 307 |
|---|---|
| 预测 | 2044 |
预览内容
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