自動運転車、ロボタクシーおよびセンサー 2022-2042年: IDTechEx

25% of passenger car miles will be driven by autonomous robotaxis by 2042

自動運転車、ロボタクシーおよびセンサー 2022-2042年

ピークカー、ロボタクシー、プライベートおよび共有モビリティ、LiDAR、レーダー、カメラ、HD マップ、5G および V2X、市場見通し


製品情報 概要 目次 価格 Related Content
近年では、レーダー、LiDAR、HD カメラやソフトウェアなどの自動運転車の大幅な改良によりロボタクシーが商用化の手前にまで準備が整いつつあります。Waymo、Cruiseおよびその他の企業による自動運転実験は現在、法制上の障壁を取り除き、自動運転サービスへと進化しつつあります。IDTechEx の新しい見通しは、これらのサービスが20年以内にどのように主流となり、潜在的需要が拡大するセンサー市場を生み出すかを検証しています。
「自動運転車、ロボタクシーおよびセンサー 2022-2042年」が対象とする主なコンテンツ(詳細は目次のページでご確認ください)
◆自動運転
o 様々なレベルの自動運転
o レベル5に利用できるルート
o センサー使用状況の概要(カメラ、LiDAR およびレーダー)
o バリューチェーンの概要
o レベル3個人用自動運転の障壁
o 他の業界における自動運転
◆個人用自動運転車両
o 近年の規制の変化
o 異なる地域の規制状況
o 確立された OEM 自動運転事業、MaaS 投資および自家用車
o 個人用自動運転センサースイートの分析
◆自動運転モビリティ・アズ・ア・サービス(MaaS)
o カリフォルニア州車両管理局(DMV)自動運転解除数の分析
o カリフォルニア州 DMV 衝突事故報告の分析
o パフォーマンスおよび試験走行距離別の市場シェア
o 新型コロナウイルスの自動運転実証実験への影響
o 有力企業の動向、新型コロナウイルスへの対応ならびにセンサースイート
o 自動運転 MaaS センサースイートの分析
◆要素技術
o センサースイートにおいてカメラ、レーダーおよび LiDAR が果たす主な役割
o 主なセンサーそれぞれの一般的トレンド
o 以下の技術の徹底詳細分析:
カメラ/赤外線/レーダー/LiDAR/自己位置推定/遠隔操作/接続性(5G、6G、LiFi、コネクテッドカー)
◆見通し 2022-2042年
o 世界の MaaS 導入
o 地域別の MaaS 導入
o 2030年代のピークカーの販売
o 地域別の自動車販売
o 技術別の自動車販売(レベル2以下、レベル3、レベル4プライベートおよびレベル4共有)
o レーダーセンサー販売台数
o MaaS 収益見通し
o MaaS CAGR 見通し
 
「自動運転車、ロボタクシーおよびセンサー 2022-2042年」は以下の情報を提供します
◆自動運転市場の現状、有力企業、主な M&A 情報、技術トレンドおよび最新/最先端技術の分析
◆20年先市場見通しおよび分析:
  • ライドシェア企業の市場拡大の分析
  • 自動運転モビリティ・アズ・ア・サービスの導入見通し
  • 既存の自家用車のサイズや耐久寿命がもたらす遅延効果の分析
  • 自動車販売およびピークカー
  • 地域別および技術別の自動車販売内訳
  • 既存 ADAS 技術の導入率
  • センサー市場の見通し
  • MaaS 市場の見通し
 
In recent years, vast improvements to autonomous vehicle technologies such as radar, lidar, HD cameras and software have propelled robotaxis to the cusp of market-readiness. Autonomous trials from Waymo, Cruise, and others are now evolving into autonomous services, with legislative barriers clearing. New IDTechEx forecasts reveal how these services will come to dominate within 20 years, creating massive opportunities for the underlying sensors market, which grows at over 30% CAGR.
 
Autonomous Vehicles
 
'Autonomous vehicle' (AV) is an umbrella term for the six levels as defined by the SAE. Today, most new cars are arriving with the option of level 2 functionality, and the industry is technically ready for level 3 once regulatory hurdles clear.
 
Some regions are even pushing for level 4 technology, but most activity here is still with autonomous mobility start-ups and in trial stages. Overall, the report finds autonomous vehicles will become a massively disruptive technology which will grow rapidly at a rate of up to 47% to transform the auto market over the next two decades.
 
SAE's six levels of autonomy. Source: SAE, IDTechEx
Generally, there are two pathways to full autonomy: incremental progression through the SAE levels, the path taken by established OEMs; and direct to level 4 with autonomous trialling (typically with retrofitted vehicles) with the aim to provide mobility-as-a-service (MaaS), the path taken by the start-ups. In both areas, the report finds significant growth and progression in Europe, the US and China.
 
Regulatory Hurdles Overcome
 
Level 3 technology has been ready since 2017, however automakers have not been able to release these autonomous features due to legislation, which has had unclear definitions about how the technology should work, and liability in the event of an accident.
 
Recently, regulations have started to improve, allowing some regions such as Japan, Germany and the UK to have level 3 vehicles on their roads by the end of 2021. IDTechEx expects significant adoption of level 3 and level 4 technology within the car market over the next 10-20 years, radicalising the way society travels and causing huge disruption to the auto sector's century old business models.
 
Source: IDTechEx
Market Leaders
 
Within this report, we analyse the strategies of OEMs and their attitudes towards autonomising their models, what features they are bringing and what sensor suites they are using, including solid state LiDAR, mechanically rotating LiDAR, radar, imaging radar, cameras, short wave infrared (SWIR) thermal cameras and more. Honda and Mercedes are the leaders here, with Honda releasing a level 3 vehicle into the Japanese market and Mercedes preparing a level 3 release in Autumn 2021. GM and Tesla are close behind with level 3 cars in waiting (technically ready but held back by regulations). In China Arcfox and Xpeng are pushing for level 3 to level 4 vehicles, but are again held back by regulations.
 
The leaders of the mobility start-ups such as Waymo, Cruise, Baidu and AutoX are well established, and their trials are progressively transforming into commercial services. From analysis of the top performing players since 2015, IDTechEx predicts that autonomous driving systems will be on par with human safety levels by the end of 2023.
 
Given the current state of trials and existing plans for further expansion from key players, IDTechEx believes 2023 will be the start of the AV revolution. IDTechEx expects that the trials will grow within their existing cities and then spread from city to city much in the same way as ride-hailing platforms (Uber, Lyft, Didi etc.) over the past decade. IDTechEx reports on the activity of the key autonomous players, their response to the COVID-19 crisis and their latest sensor suites.
 
Source: IDTechEx
Sensor Trends
 
The report finds that autonomy will create massive opportunities in the automotive sensors industry. IDTechEx reports on the progress in cameras (including infrared and event-based detection), radar, LiDAR and supporting technologies such as connectivity and teleoperation. Within the key three sensors we see adoption of higher resolution cameras and higher frame rates, advancements in the performance and power of radar and significant cost reductions in LiDAR. Our analysis is based on primary information, with the report containing 8+ primary interviews from key players.
 
Source: IDTechEx
 
Key topics covered:
 
  • Private AVs
  • Shared and mobility as a service (MaaS) AVs
  • Regional breakdowns in analysis and forecasts: US, China, Europe, RoW
  • Key player activities in autonomous mobility as a service with primary interviews
  • Camera, Radar and LiDAR suite analysis
  • Key regulatory barriers and changes (by region)
  • Key safety certifier changes which will mandate sensor adoption in coming years
  • Key trends in enabling technologies; Camera (including infrared), radar, LiDAR, connectivity, localisation, HD mapping and more.
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アイディーテックエックス株式会社 (IDTechEx日本法人)
担当: 村越美和子 m.murakoshi@idtechex.com
Table of Contents
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
 

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自動運転車、ロボタクシーおよびセンサー 2022-2042年

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レポート概要

スライド 364
フォーキャスト 2042
発行日 Aug 2021
ISBN 9781913899660
 

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