Global Heavy-duty autonomous trucks sales revenue is to exceed US$120 billion by 2044.

Autonomous Trucks 2024-2044: Technologies, Trends, Forecasts

Heavy-duty autonomous trucks - US, EU, and Chinese truck market, key players, trends, safety, regulations, industry analysis, autonomous truck sensors, Mobility as a Service (MaaS), enabling technologies, and market forecasts.


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In recent years, the development of autonomous driving technology in the trucking industry has progressed rapidly, with numerous technology companies and truck manufacturers initiating commercial trials of autonomous heavy-duty trucks. Although full commercialization remains some distance away, the advancements in this technology offer substantial potential for addressing current industry pain points.
 
Critical Challenges in the Trucking Industry
Today, the trucking industry faces critical challenges, including high operational costs, driver management issues, and safety concerns. According to the American Trucking Association (ATA), the U.S. trucking industry faced a shortfall of more than 100,000 drivers, a number that could increase to 160,000 by 2028. The industry needs to hire one million drivers over the next decade to bridge this gap, replace retiring drivers, and meet growing shipping demands. Attracting younger generations is particularly challenging as they are reluctant to spend extended periods away from home. The aging population in Asian countries like China is expected to exacerbate labor issues in the next decade. The driver shortage has significantly increased international freight transportation costs and operational expenses for trucking companies. Autonomous heavy-duty trucks offer a solution to these challenges. Autonomous driving systems can eliminate distractions and human errors, thereby improving safety. They also enable more efficient communication with other vehicles and devices, streamlining operations and enhancing overall efficiency.
 
Industry Dynamics and Market Shifts
IDTechEx has observed significant activity in the heavy-duty long-haul trucking sector, some companies, such as Inceptio, Deepway, and Einride, have advanced from the proof-of-concept stages to securing small-scale commercial opportunities. However, the fall of several industry giants, including TuSimple, Waymo, and Embark, underscores the significant challenges faced in the autonomous trucks sector. This highlights the volatile nature of the industry and the hurdles in transitioning from innovation to large-scale commercial viability. Autonomous trucks were once considered one of the most commercially valuable and rapidly monetizable products in the autonomous driving sector, with American and European companies playing pivotal roles in their early development. However, post-pandemic global freight fluctuations and the pace of autonomous driving regulation implementation have slowed commercialization. Leading companies such as Waymo and TuSimple have either exited the autonomous trucking market or scaled down their strategic focus.
 
As regulations for autonomous driving gradually take shape to drive commercialization, Total Cost of Ownership (TCO) will become a decisive factor in determining the penetration rate of autonomous truck systems. Due to the extensive operating hours of trucks, energy consumption and labor costs are major factors influencing the TCO of autonomous trucks. Although the industry claims that autonomous truck systems can reduce costs by over 15% and eliminate labor costs, IDTechEx has conducted practical calculations to determine the TCO for different levels of autonomous systems (L0, L2, L3, L4) across various markets. Additionally, IDTechEx has compared the TCO of internal combustion engine (ICE) platforms with electric platforms.
 
Comprehensive Analysis and Future Outlook
This report explains the reasons behind the development of the autonomous trucking industry, including policy support for autonomous vehicles and trucks in Asia, the U.S., and Europe, future policy forecasts, and the movements of over a dozen key players based on IDTechEx's proprietary research, which includes 20-year forecast data.
 
IDTechEx's in-depth research into the Chinese market highlights the value of China's autonomous truck sector and the capabilities of its startups such as Inceptio, Plus.ai, Pony.ai, TrunkTech. IDTechEx also examines different drivetrain configurations for autonomous trucks and proposes correlations between energy types and vehicle performance.
 
Key Aspects
This report on Autonomous Trucks 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 Autonomous trucking industry
  • A summary of each player's product, commercialization, and activity
  • Autonomous trucking vehicle 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 Autonomous trucking sales and revenue
Report MetricsDetails
Historic Data2019 - 2023
CAGRThe global heavy-duty autonomous trucks market across all drivetrains will reach 1,252k units by 2044, compared to 13.1k in 2024 - a CAGR of 25.61% over 20 years.
Forecast Period2024 - 2044
Forecast Unitsunits, US$
Regions CoveredWorldwide, United States, China, Europe
Segments CoveredTrucks, electric trucks, autonomous trucks, sensors, transport as a service, trucking as a service, autonomous hub to hub
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Table of Contents
1.EXECUTIVE SUMMARY
1.1.Autonomous trucking - Industry Overview
1.2.Autonomous trucking - the right conditions right now
1.3.Why Automate Trucks?
1.4.Number Of Active Companies
1.5.The Sensor Trio
1.6.Different powertrains for different vehicles
1.7.Business model options for autonomous trucks
1.8.SWOT analysis for autonomous trucks
1.9.IDTechEx predicted timelines
1.10.Heavy-Duty Trucking Unit Sales 2019-2044
1.11.Heavy-Duty Trucking Revenue 2023-2044
1.12.Sensors for heavy-duty trucks 2021-2044
1.13.Access More with an IDTechEx Subscription
2.AUTONOMOUS TRUCKS: PLAYERS AND ANALYSIS
2.1.Introduction
2.1.1.Pain points in the trucking industry
2.1.2.Why Automate Trucks?
2.1.3.SAE levels of automation
2.1.4.Level-2 and Level-4 Trucking
2.1.5.Level-4 MaaS for trucking
2.1.6.Authorities for regulating autonomous driving - US
2.1.7.Authorities for regulating autonomous driving - China
2.1.8.Authorities for regulating autonomous driving - EU
2.2.Players - Start-ups
2.2.1.Startups list
2.2.2.TuSimple - Overview
2.2.3.TuSimple - Overview
2.2.4.TuSimple's Timeline
2.2.5.Perception system of TuSimple's autonomous trucks
2.2.6.Plus - Overview
2.2.7.Plus - Sensor Suite
2.2.8.Plus - Testing, Trials and Deployments
2.2.9.Inceptio - Overview
2.2.10.Inceptio - Sensor Suite
2.2.11.Inceptio - Testing
2.2.12.Inceptio - Partners and Customers
2.2.13.Einride - Overview
2.2.14.Einride: A closer look into the T-pod and E-truck
2.2.15.Einride - Partners
2.2.16.Einride - The Grids Concept
2.2.17.Einride - The Grids Concept
2.2.18.Kodiak Robotics - Overview
2.2.19.Kodiak - Sensor Suite
2.2.20.Kodiak - Trials and Business Model (1)
2.2.21.Kodiak - Trials and Business Model (2)
2.2.22.Torc Robotics - Overview
2.2.23.Torc Robotics - Sensor Suite (Gen 2)
2.2.24.Torc Robotics - Testing and Trials (1)
2.2.25.Torc Robotics - Testing and Trials (2)
2.2.26.Aurora
2.2.27.Aurora - Sensor Suite
2.2.28.Aurora - Trials, Rollout and Business Model
2.2.29.Pony.ai
2.2.30.Pony.ai - Business Model
2.2.31.DeepWay - A Baidu Founded Start-up
2.2.32.DeepWay - Sensor Suites
2.2.33.DeepWay - Trials (1)
2.2.34.DeepWay - Trials (2)
2.2.35.Terraline (Formerly Solo AVT)
2.2.36.TrunkTech
2.3.Players - Established Truck OEMs
2.3.1.Volvo Truck - Overview
2.3.2.Volvo Truck - Vera and VNL
2.3.3.Tesla
2.3.4.Daimler (1)
2.3.5.Daimler (2)
2.3.6.MAN
2.3.7.Scania
2.3.8.Hyundai
2.4.Trucking Players That Are No Longer Active
2.4.1.Embark - Overview
2.4.2.Waymo - Background
2.5.Redundancy in Autonomous Trucks
2.5.1.Redundancy in Different Systems
2.5.2.Redundant Systems
2.5.3.Daimler Trucks - Redundancy in Braking Control
2.5.4.Daimler Trucks - Steering and Communication
2.5.5.Continental - Brakes (not Heavy-Duty Specific)
2.5.6.Bosch - Brakes and Steering (not Heavy-Duty Specific)
2.5.7.TuSimple - Functional Safety
2.5.8.TuSimple - Hardware Failure Tolerance
2.5.9.TuSimple - Software Fault Tolerance
2.5.10.TuSimple - Functional Safety Overview
2.5.11.Plus.AI - Single Sensor Type Redundancy
2.5.12.Kodiak - Localisation Redundancy
2.5.13.Aurora
2.5.14.Mobileye - A Different Approach to Redundancy
2.5.15.Redundancy in Connected Technologies
2.6.Truck analysis
2.6.1.Technology Maturity Status Definitions
2.6.2.Market readiness level of L4 autonomous truck companies 2022
2.6.3.Market Readiness Level of L4 Autonomous Truck Companies 2024
2.6.4.Maturity
2.6.5.Fundings Raised and Received (1)
2.6.6.Fundings Raised and Received (2)
2.6.7.Fundings
2.6.8.Fundings
2.6.9.Testing Mileage, Trails and Partners
2.6.10.L4 Autonomous Public Road Testing Time, Location, Speed and Approval Authority
2.6.11.Company Backgrounds
2.6.12.Autonomous Trucking Activity (1)
2.6.13.Autonomous Trucking Activity (2)
2.6.14.Total Cost of Ownership Analysis - Assumption and Breakdown
2.6.15.Total Cost of Ownership Analysis
2.6.16.Total Cost of Ownership Analysis
2.6.17.Company Locations (1)
2.6.18.Company Locations (2)
2.6.19.Business Model Options for Start-ups
2.6.20.Business and Operating Model Adoption
2.6.21.6 Key Drivers for Autonomous Trucks (1)
2.6.22.6 Key Drivers for Autonomous Trucks (2)
2.6.23.Remaining Hurdles for Autonomous Trucks
2.6.24.IDTechEx opinion
3.SUMMARY OF AUTONOMOUS ACTIVITY AND PROGRESS IN TRUCKS
3.1.Locations Split by Vehicle Types
3.2.Table of Vehicles and Value Chain Position of Companies in Commercial Autonomy
3.3.Ones to Watch - Autonomous Trucks
3.4.SWOT analysis and comparisons for autonomous trucks
3.5.Commercial readiness and opportunity comparison, roboshuttle, autonomous buses, autonomous trucks.
3.6.IDTechEx predicted timelines
4.ENABLING TECHNOLOGIES: CAMERAS
4.1.Cameras in Autonomous Trucks
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 Autonomous Trucks
5.2.Segmenting the Electromagnetic Spectrum
5.3.The Need for NIR
5.4.OmniVision: Making Silicon CMOS Sensitive to NIR
5.5.OmniVision: Making Silicon CMOS Sensitive to NIR
5.6.Motivation For Short-Wave Infra-Red (SWIR) Imaging
5.7.Why SWIR in Autonomous Mobility
5.8.Other SWIR Benefits: Better On-Road Hazard Detection
5.9.SWIR Sensitivity of Materials
5.10.SWIR Imaging: Incumbent and Emerging Technology Options
5.11.The Challenge of High Resolution, Low Cost IR Sensors
5.12.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.QD-Si CMOS at IR and NIR
6.7.Hybrid QD-Si Global Shutter CMOS at IR and NIR
6.8.Emberion: QD-Graphene SWIR Sensor
6.9.Emberion: QD-Graphene-Si Broadrange SWIR sensor
6.10.SWIR Vision Sensors: First QD-Si Cameras and/or an Alternative to InVisage?
6.11.SWIR Vision Sensors: First QD-Si Cameras and/or an Alternative to InVisage?
6.12.SWIR Vision Sensors: First QD-Si Cameras and/or an Alternative to InVisage?
6.13.SWIR Vision Sensors: First QD-Si Cameras and/or an Alternative to InVisage?
6.14.QD-ROIC Si-CMOS integration examples (IMEC)
6.15.QD-ROIC Si-CMOS Integration Examples (RTI International)
6.16.QD-ROIC Si-CMOS Integration Examples (ICFO)
6.17.QD-ROIC Si-CMOS Integration Examples (ICFO)
7.ENABLING TECHNOLOGIES: LIDAR
7.1.LiDAR in Autonomous Trucks
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 Autonomous Trucks
8.2.Typical Sensor Suite for Autonomous Cars
8.3.Radar Has a Key Place in Automotive Sensors
8.4.The Need for and Emergence of Imaging Radar
8.5.4D Radars and Imaging Radars
8.6.Radar Trends: Volume and Footprint
8.7.Radar Trends: Packaging and Performance
8.8.Radar Trends: Increasing Range
8.9.Radar Trends: Field of View
8.10.Radar Trilemma
8.11.Radar Anatomy
8.12.Radar Key Components
8.13.Primary Radar Components - The Antenna
8.14.Primary Radar Components - the RF Transceiver
8.15.Primary Radar Components - MCU
8.16.Automotive Radars: Frequency Trends
8.17.Trends in Transceivers
8.18.Two Approaches to Larger Channel Counts
8.19.Semiconductor Technology Trends in Radar
8.20.Funding for Radar Start-ups
8.21.Future Radar Packaging Choices
8.22.Leading players - tier 1 suppliers
8.23.Transceiver suppliers
8.24.Supply chain
8.25.Example products from a tier 1 - Continental
8.26.Example products from a tier 1 - Bosch
8.27.Example of radar start-up - Arbe
8.28.Arbe and its Investors
8.29.Example of radar start-up - Zadar
9.FORECASTS
9.1.Notes on the forecasts chapter
9.2.Method
9.3.Heavy-Duty Trucking Unit Sales 2023-2044
9.4.Autonomous truck pricing 2018-2044
9.5.Heavy-Duty Trucking Revenue 2023-2044
9.6.Miles and service revenue for autonomous trucks 2023-2044
9.7.Autonomous truck powertrains 2023-2044
9.8.Sensors for autonomous trucks 2021-2044
10.COMPANY PROFILES
10.1.Aurora
10.2.Bosch
10.3.Continental AG
10.4.DeepWay
10.5.Einride: Automating Logistics
10.6.Hyundai
10.7.Inceptio
10.8.Innoviz
10.9.Innoviz
10.10.Kodiak Robotics: Autonomous Trucking Start-up
10.11.Mobileye: ADAS & Autonomy Computation
10.12.Ouster
10.13.plus.ai
10.14.Pony.ai
10.15.Terraline
10.16.Tesla Motors
10.17.Torc: An Autonomous Technology Company in Roboshuttles and Trucks
10.18.Trunk Tech
10.19.TuSimple
10.20.Valeo
10.21.Velodyne LIDAR
10.22.Volvo Trucks - Truck Electrification
10.23.Waymo (2023)
10.24.Waymo (2024)
 

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Report Statistics

Slides 248
Companies 24
Forecasts to 2044
Published Jun 2024
ISBN 9781835700457
 

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