Vehículos autónomos de servicio pesado 2023-2043: camiones, autobuses y lanzaderas automáticas: IDTechEx

Autonomous trucks have the greatest long-term potential, with a 15-year CAGR of 47%

Vehículos autónomos de servicio pesado 2023-2043: camiones, autobuses y lanzaderas automáticas

Actores clave, análisis de la industria, tecnologías habilitadoras y pronósticos del mercado


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IDTechEx has found significant activity in the autonomous heavy-duty commercial vehicle space, with hundreds of vehicles in various stages of trialling globally, and some companies on the precipice of fully unmanned commercial deployment. Each industry has unique strengths and challenges, this report explains them and gives market forecasts accordingly.
 
 
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Roboshuttles
Roboshuttles are a new and exciting form of transport which has seen the most player activity. Leaders in the field, EasyMile and Navya, have been in the game since 2014 and have accumulated around two thirds of all roboshuttle sales between them. But their sales in recent years have been dwindling. Despite this there have been recent pushes from China, with Yutong looking to put a large fleet on the road for large trials in 2022. IDTechEx has observed many small trial deployments of roboshuttles with less than five vehicles being tested by the public on very restricted routes. There have been some bigger deployments, such as Apolong in 2019, but it seems that the industry is getting stuck at turning these large pilots into commercial trials. This report looks at some of the biggest stumbling blocks for roboshuttles and considers when these may be overcome and how quickly the market can grow afterwards. One thing is certainly clear, with many trial activities in tens of cities around the world, when commercial deployments do happen, growth will be rapid.
 
Autonomous buses
While autonomous buses have seen less activity than roboshuttles, bus automation is not promising to revolutionize public transport. Here IDTechEx thinks that automation is going to provide iterative improvements to existing vehicles over the coming years. The advantage that buses have over roboshuttles is that the driver and conventional controls can remain in the vehicle during trials. Since both will be forced to operate near pedestrians, human supervision will likely be required for many years. Many bus companies see the driver transitioning to a supervisory role as the technology improves, with long term ambitions of uncrewed autonomous buses. Despite this, level 4 autonomy can bring benefits to buses today. The autonomy level will improve bus safety, and deployments in special use cases, such as in bus depots, airside airport buses and minibuses operating on controlled access campuses, could be accomplished in the next few years.
 
The big challenge for autonomous buses is simply the size of the industry today. Of the three heavy-duty sectors covered in this report, autonomous buses have the fewest vehicles on the road, with a total fleet size in the low tens, compared to the mid hundreds for both roboshuttles and autonomous trucks. It is likely that this is due to the expense of working on automating buses and this is reflected with approximately two thirds of the autonomous bus activity coming from established OEMs rather than autonomous start-ups which are more dominant in roboshuttles and autonomous trucks.
 
 
Autonomous Trucks
Out of roboshuttles, autonomous buses and autonomous trucks, IDTechEx believes that trucks make the most compelling case for automation. There is a measurable need for truckers in China, the US and Europe. This once popular profession is failing to attract younger generations due to the long hours on the road and separation from family. The average age of truck drivers is increasing, and the industry is heading for a crisis as demand for haulage soars. This is the key driver for autonomous trucks, but it doesn't matter if the task is unachievable. Thankfully, autonomous trucks also have an achievable operational design domain, and therefore a promising route to deployment. In China and the US, many of the miles served by trucks are between distribution centres separated by vast stretches of open highway. These roads are not used by pedestrians, they have central reservations and flow in only one direction either side and are generally well maintained. This drastically reduces the challenges that autonomous systems in roboshuttles and autonomous buses encounter when operating in densely populated cities, with less predictable traffic and unpredictable pedestrians. Operating at night, when roads are quieter, also does not impact the value of the mission, unlike roboshuttles and autonomous buses whose operation is most valuable around peak travel times.
 
Key aspects
This report on heavy-duty autonomous vehicles provides detailed analysis of the companies and activities within autonomous commercial vehicles: roboshuttles, autonomous buses and autonomous trucks. Key challenges and opportunities are identified for each industry and predictions regarding their commercial deployment are made. The high-fidelity analysis of each market guides IDTechEx's 20-year forecasts.
 
Market Forecasts & Analysis:
20-year forecasts for roboshuttles, autonomous buses, and autonomous trucks
  • Unit sales
  • Revenue from vehicle sales
  • Revenue from commercial services
  • Adoption of electric powertrains
  • Sensors for heavy-duty autonomous vehicles
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Table of Contents
1.EXECUTIVE SUMMARY
1.1.Heavy-duty autonomous vehicles report
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.Autonomous trucking - the right conditions right now
1.7.Why Automate Trucks?
1.8.Technology Readiness
1.9.Different powertrains for different vehicles
1.10.Types of service for roboshuttles and buses
1.11.Business model options for autonomous trucks
1.12.Number Of Active Companies
1.13.The Sensor Trifactor
1.14.Sensor suites for heavy-duty autonomous vehicles
1.15.SWOT analysis and comparisons for roboshuttles, autonomous buses and autonomous trucks.
1.16.Commercial readiness and opportunity comparison, roboshuttle, autonomous buses, autonomous trucks.
1.17.IDTechEx predicted timelines
1.18.Roboshuttle fleet size and unit sales 2020-2043
1.19.Roboshuttle revenues, vehicle sales and passenger fares 2020-2043
1.20.Autonomous bus unit sales 2023-2043
1.21.Autonomous bus revenue 2023-2043
1.22.Heavy Duty Trucking Unit Sales 2022-2042
1.23.Heavy Duty Trucking Revenue 2022-2042
1.24.Heavy Duty Autonomous Unit Sales 2023-2043
1.25.Heavy Duty Autonomous Revenue 2023-2043
1.26.Sensors for Heavy Duty Autonomous Vehicles 2023-2043
1.27.Access to 20 IDTechEx Portal Company Profiles
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 (1)
2.1.5.Table Comparison Of Active Companies (2)
2.1.6.Table Comparison Of Active Companies (3)
2.1.7.Funding
2.1.8.EasyMile
2.1.9.EasyMile Real World Trials And Testing
2.1.10.EasyMile Business Model
2.1.11.Navya
2.1.12.Navya Testing Locations
2.1.13.Navya Use Case Examples
2.1.14.Navya's Installation Process
2.1.15.Navya's Business Model
2.1.16.ZF - A Robot Shuttle Future.
2.1.17.ZF - Robot Shuttle Deployment
2.1.18.ZF 2getthere Trial in Saudi Arabia
2.1.19.Coast
2.1.20.Cruise Origin
2.1.21.Toyota e-PALETTE
2.1.22.Sensible 4 - GACHA
2.1.23.IAV and the HEAT project
2.1.24.Lohr, Torc and Transdev
2.1.25.Torc/Lohr i-Cristal Sensor Suite
2.1.26.May Mobility
2.1.27.NEVS
2.1.28.Ohmio - Lift
2.1.29.Yutong
2.1.30.Apollo - Autonomous Branch of Baidu
2.1.31.Higer
2.1.32.Zoox
2.1.33.Zoox Sensor Suite
2.1.34.Service Providers
2.2.Roboshuttle projects that have discontinued
2.2.1.Continental
2.2.2.Bosch
2.2.3.Local Motors - Olli
2.2.4.e.Go Moove
2.2.5.DGWORLD
2.2.6.Projects That Are No Longer Active (1)
2.2.7.Projects That Are No Longer Active (2)
2.2.8.Projects That Are No Longer Active (3)
2.3.Roboshuttles analysis and conclusions
2.3.1.Technology Readiness
2.3.2.Decline in Roboshuttle Companies
2.3.3.Technology Readiness - Still Active
2.3.4.Where Players Exit
2.3.5.Where Are Players In The Value Chain
2.3.6.Speed And Distance
2.3.7.Passenger Capacity
2.3.8.Total Cost of Ownership Analysis
2.3.9.Reasons Roboshuttles Will Succeed
2.3.10.Reasons Roboshuttles Will Fail
2.3.11.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)
3.1.8.Table Comparison Of Active Players (3)
3.2.Players - Minibuses
3.2.1.King Long
3.2.2.Aurrigo
3.2.3.Hyundai Autonomous Bus
3.2.4.Volkswagen
3.2.5.Volkswagen ID.Buzz - Sensor Suite
3.2.6.Volkswagens MOIA Project
3.2.7.Perrone Robotics - Overview
3.2.8.Perrone Robotics - Sensor Suite
3.2.9.Perrone Robotics - Deployment And Planned Rollout
3.3.Players - Midibuses
3.3.1.ADASTEC and Karsan
3.3.2.ADASTEC And Karsan - Sensor Suite
3.3.3.Golden Dragon ASTAR
3.3.4.QCraft
3.3.5.QCraft - Sensor Suite
3.3.6.LILEE
3.3.7.Zhongtong
3.4.Players - City Buses
3.4.1.Fusion Processing - Overview
3.4.2.Fusion Processing - Testing and Trials
3.4.3.ST Engineering
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.Irizar
3.4.8.Iveco
3.4.9.DeepBlue
3.5.Companies No Longer Active In Autonomous Buses
3.5.1.Daimler
3.5.2.Scania
3.5.3.Proterra
3.5.4.Other Big Players Either Not Involved Or Stopped
3.6.Autonomous Bus Analysis
3.6.1.Bus Sizes
3.6.2.Activity
3.6.3.Technology Readiness
3.6.4.Few Large Trials
3.6.5.Vehicle Type Vs Company Type
3.6.6.Lack Of Start-Ups, Driven By Established OEMs
3.6.7.Options For Early Deployments Of Autonomous Tech
3.6.8.Autonomous Bus Deployments In Other ODDs
3.6.9.Companies Spread Across The World
3.6.10.Drivetrains - Most Are Thinking Electric
3.6.11.Reasons Autonomous Buses Will Be A Success
3.6.12.Reasons Autonomous Buses Will Fail
3.6.13.IDTechEx Opinion On Autonomous Buses
4.AUTONOMOUS TRUCKS: PLAYERS AND ANALYSIS
4.1.Introduction
4.1.1.Pain points in the trucking industry
4.1.2.Why Automate Trucks?
4.1.3.SAE levels of automation
4.1.4.Level-2 And Level-4 Trucking
4.1.5.Level-4 MaaS for trucking
4.1.6.Authorities for regulating autonomous driving
4.1.7.The Autonomous Legal Race
4.2.Players - Start-ups
4.2.1.Funding and Maturity
4.2.2.TuSimple - Overview
4.2.3.TuSimple's AFN
4.2.4.TuSimple's unique perception solution
4.2.5.Perception system of TuSimple's autonomous trucks
4.2.6.TuSimple's enhanced night vision camera system
4.2.7.World's first fully autonomous semi-truck operating on public roads without human intervention
4.2.8.TuSimple's Business Model
4.2.9.Embark - Overview
4.2.10.Embark - Sensors
4.2.11.Embark - Trials And Rollout
4.2.12.Einride - Overview
4.2.13.Einride: a closer look into the T-pod and E-truck
4.2.14.Kodiak Robotics - Overview
4.2.15.Kodiak - Sensor Suite
4.2.16.Kodiak - Trials And Business Model
4.2.17.Plus - Overview
4.2.18.Plus - Sensor Suite
4.2.19.Plus - Testing, Trials and Deployments
4.2.20.Inceptio - Overview
4.2.21.Inceptio - Sensor Suite
4.2.22.Inceptio - Driverless Test
4.2.23.Waymo - Background
4.2.24.Waymo - Sensor Suite
4.2.25.Waymo - Trials
4.2.26.Torc Robotics - Overview
4.2.27.Torc Robotics - Sensor Suite
4.2.28.Torc Robotics - Testing And Trials
4.2.29.Aurora
4.2.30.Aurora - Sensor Suite
4.2.31.Aurora - Trials, Rollout And Business Model
4.2.32.Pony.ai
4.2.33.Pony.ai Sensor Suite (Robotaxi version)
4.2.34.Tesla
4.2.35.Solo AVT
4.2.36.DeepWay - A Baidu Founded Start-up
4.3.Player - Established Truck OEMs
4.3.1.Volvo Truck - Overview
4.3.2.Volvo Truck - Vera And VNL
4.3.3.Daimler
4.3.4.MAN
4.3.5.Scania
4.3.6.Hyundai catching up in the autonomous trucking race
4.4.Trucking Players That Are No Longer Active
4.4.1.Why Starsky Robotics Failed
4.4.2.Ike
4.4.3.Uber and Otto
4.5.Redundancy In Autonomous Trucks
4.5.1.Redundancy in Different Systems
4.5.2.Redundant Systems
4.5.3.Daimler Trucks - Redundancy in Braking Control
4.5.4.Daimler Trucks - Steering and Communication
4.5.5.Continental - Brakes (not Heavy Duty Specific)
4.5.6.Bosch - Brakes and Steering (not Heavy Duty Specific)
4.5.7.TuSimple - Functional Safety
4.5.8.TuSimple - Hardware Failure Tolerance
4.5.9.TuSimple - Software Fault Tolerance
4.5.10.TuSimple - Functional Safety Overview
4.5.11.Plus.AI - Single Sensor Type Redundancy
4.5.12.Kodiak - Localisation Redundancy
4.5.13.Aurora
4.5.14.Mobileye - A Different Approach To Redundancy
4.5.15.Redundancy in Connected Technologies
4.6.Truck analysis
4.6.1.Technology Maturity Status Definitions
4.6.2.Market readiness level of L4 autonomous truck companies
4.6.3.Maturity
4.6.4.Testing Distances
4.6.5.Company Backgrounds
4.6.6.Autonomous Trucking Activity
4.6.7.Company Locations
4.6.8.Business Model Options For Start-ups
4.6.9.Business Model Adoption
4.6.10.Key Drivers For Autonomous Trucks
4.6.11.Key Drivers For Autonomous Trucks
4.6.12.Remaining Hurdles For Autonomous Trucks
4.6.13.IDTechEx Opinion
5.SENSOR SUITES AND COMPUTERS FOR COMMERCIAL AUTONOMOUS VEHICLES
5.1.The Sensor Trifactor
5.2.Sensors for Roboshuttles
5.3.Sensors for autonomous buses
5.4.Sensors for autonomous trucks
5.5.Comparison to robotaxis
5.6.Computation for heavy-duty autonomous vehicles
5.7.Main computer supplier - Nvidia
5.8.Main computer supplier - Mobileye
5.9.Main LiDAR suppliers - Velodyne and Ouster
5.10.Sensor suite attributes
5.11.Conclusions
6.SUMMARY OF AUTONOMOUS ACTIVITY AND PROGRESS ACROSS TRUCKS, BUSES, ROBOSHUTTLES
6.1.Number Of Active Companies
6.2.Big Map of Activity Across The World
6.3.Locations Split By Vehicle Types
6.4.Table Of Vehicles And Players
6.5.Value Chain Position Of Companies In Commercial Autonomy
6.6.Technology Readiness
6.7.Ones To Watch - Roboshuttles (1)
6.8.Ones To Watch - Roboshuttles (2)
6.9.Ones to watch - Autonomous buses (1)
6.10.Ones to watch - Autonomous buses (2)
6.11.Ones To Watch - Autonomous Trucks
6.12.SWOT analysis and comparisons for roboshuttles, autonomous buses and autonomous trucks.
6.13.Commercial readiness and opportunity comparison, roboshuttle, autonomous buses, autonomous trucks.
6.14.IDTechEx predicted timelines
6.15.Conclusions
7.ENABLING TECHNOLOGIES: CAMERAS
7.1.RGB/Visible light camera SWOT
7.2.CMOS image sensors vs CCD cameras
7.3.Key Components of CMOS
7.4.Front vs backside illumination
7.5.Reducing Cross-talk
7.6.Global vs Rolling Shutter
7.7.TPSCo: leading foundry for global shutter
7.8.Sony: CMOS Breakthrough?
7.9.Sony: BSI global shutter CMOS with stacked ADC
7.10.OmniVision: 2.µm global shutter CMOS for automotive
7.11.Hybrid organic-Si global shutter CMOS
7.12.Event-based Vision: a New Sensor Type
7.13.What is Event-based Sensing?
7.14.General event-based sensing: Pros and cons
7.15.What is Event-based Vision? (I)
7.16.What is Event-based Vision? (II)
7.17.What is event-based vision? (III)
7.18.What does event-based vision data look like?
7.19.Event Based Vision in Autonomy?
8.ENABLING TECHNOLOGIES: THERMAL CAMERAS
8.1.Segmenting the Electromagnetic Spectrum
8.2.Thermal camera SWOT
8.3.IR Cameras
8.4.The Need for NIR
8.5.OmniVision: Making Silicon CMOS Sensitive to NIR
8.6.OmniVision: Making Silicon CMOS Sensitive to NIR
8.7.Motivation For Short-Wave Infra-Red (SWIR) Imaging
8.8.Why SWIR in Autonomous Mobility
8.9.Other SWIR Benefits: Better On-Road Hazard Detection
8.10.SWIR Sensitivity of Materials
8.11.SWIR Imaging: Incumbent and Emerging Technology Options
8.12.The Challenge of High Resolution, Low Cost IR Sensors
8.13.Silicon Based SWIR Detection - TriEye
9.ENABLING TECHNOLOGIES: QUANTUM DOTS AS OPTICAL SENSOR MATERIALS FOR IR, NIR, SWIR
9.1.Quantum Dots as Optical Sensor Materials
9.2.Quantum Dots: Choice of the Material System
9.3.Other Ongoing Challenges
9.4.Advantage of Solution Processing
9.5.QD-Si CMOS at IR and NIR
9.6.Hybrid QD-Si Global Shutter CMOS at IR and NIR
9.7.Emberion: QD-Graphene SWIR Sensor
9.8.Emberion: QD-Graphene-Si Broadrange SWIR sensor
9.9.SWIR Vision Sensors: First QD-Si Cameras and/or an Alternative to InVisage?
9.10.QD-ROIC Si-CMOS integration Examples (IMEC)
9.11.QD-ROIC Si-CMOS Integration Examples (RTI International)
9.12.QD-ROIC Si-CMOS Integration Examples (ICFO)
9.13.QD-ROIC Si-CMOS Integration Examples (ICFO)
10.ENABLING TECHNOLOGIES: LIDAR
10.1.LiDAR classifications
10.2.Automotive LiDAR: Operating process
10.3.Automotive LiDAR: Requirements
10.4.LiDAR system
10.5.LiDAR working principle
10.6.Laser range finder function for the first production car
10.7.Comparison of lidar product parameters
10.8.TOF vs. FMCW LiDAR
10.9.LiDAR scanning categories
10.10.Comparison of Common Beam Steering Options
10.11.Overview of beam steering technologies
10.12.Summary of lidars with various beam steering technologies
10.13.Point cloud
10.14.LiDAR signal applications
10.15.3D point cloud modelling
10.16.LiDAR challenges
10.17.Poor weather performance: challenges & solutions
10.18.Autonomous mobility goes beyond cars
10.19.Early possible adoption of LiDAR
10.20.Velodyne lidar portfolios
10.21.Valeo SCALA
10.22.Livox: Risley prisms
10.23.Automotive lidar players by technology
11.ENABLING TECHNOLOGIES: RADAR
11.1.Radar SWOT
11.2.Radars are common in private vehicles
11.3.Radar Has a Key Place in Automotive Sensors
11.4.Front Radar Applications
11.5.The Role of Side Radars
11.6.Radars Limited Resolution
11.7.Radar Performance Trends
11.8.Radar Trilemma
11.9.Radar Anatomy
11.10.Primary Radar Components - The Antenna
11.11.Primary Radar Components - The RF Transceiver
11.12.Primary Radar Components - MCU
11.13.Automotive Radars: Frequency Trends
11.14.Trends in Transceivers
11.15.Two Approaches to Larger Channel Counts
11.16.Radar Board Trends
11.17.Radar Suppliers: Tier 1s and Start Ups
11.18.Leading players - tier 1 suppliers
11.19.Transceiver suppliers
11.20.Supply chain
11.21.Example products from a tier 1 - Continental
11.22.Example products from a tier 1 - Bosch
11.23.Example of radar start-up - Arbe
11.24.Arbe and its Investors
11.25.Example of radar start-up - Zadar
12.FORECASTS
12.1.Notes on the forecasts chapter
12.2.Forecasts: Roboshuttles
12.2.1.Method
12.2.2.Vehicle assumptions
12.2.3.Cities Considered
12.2.4.Adoption within cities
12.2.5.Current and forecasted city roll out 2020-2043
12.2.6.Distribution of roboshuttle cities
12.2.7.Roboshuttle fare pricing for different economies
12.2.8.Roboshuttle price decline
12.2.9.Roboshuttle fleet size and unit sales 2020-2043
12.2.10.Roboshuttle revenues, vehicle sales and passenger fares 2020-2043
12.2.11.Sensors for roboshuttles 2020-2043
12.3.Forecasts: Autonomous Buses
12.3.1.Method
12.3.2.Minibus utilization, adoption and city roll-out
12.3.3.Autonomous bus adoption
12.3.4.Autonomous bus unit sales 2023-2043
12.3.5.Vehicle pricing
12.3.6.Autonomous bus revenue 2023-2043
12.3.7.Seating capacity in autonomous buses and roboshuttles
12.3.8.Roboshuttle and autonomous bus sales revenue 2023-2043
12.3.9.Powertrains of autonomous buses 2023-2043
12.3.10.Sensors for autonomous buses
12.4.Forecasts: Autonomous Trucking
12.4.1.Method
12.4.2.Heavy Duty Trucking Unit Sales 2022-2042
12.4.3.Autonomous truck pricing
12.4.4.Heavy Duty Trucking Revenue 2022-2042
12.4.5.Miles and service revenue for autonomous trucks 2023-2043
12.4.6.Autonomous truck powertrains 2023-2043
12.4.7.Sensors for autonomous trucks
12.5.Forecast: Unit sales and sales revenues for roboshuttles, autonomous buses and autonomous trucks combined
12.5.1.Heavy duty autonomous unit sales: 2023-2043
12.5.2.Heavy-duty autonomous revenue 2023-2043
12.5.3.Sensors for heavy duty autonomous vehicles 2023-2043
 

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Report Statistics

Slides 403
Forecasts to 2043
Published Aug 2022
ISBN 9781915514141
 

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