Artificial Intelligence in Transportation Market Size, Share, and Trends

Artificial Intelligence in Transportation Market (By Offering: Hardware, Software; By Machine Learning Technology: Deep Learning, Computer Vision, Context Awareness, Natural Language Processing; By Process: Signal Recognition, Object Recognition, Data Mining; By Application) - Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2023-2032

  • Last Updated : September 2023
  • Report Code : 1983
  • Category : ICT

Artificial Intelligence in Transportation Market Size to Reach USD 23.11 Bn by 2032

The global artificial intelligence (AI) in transportation market size was valued at USD 3 billion in 2022 and is expected to reach around USD 23.11 billion by 2032, poised to grow at a compound annual growth rate (CAGR) of 22.70% over the forecast period 2023 to 2032.

Artificial Intelligence in Transportation Market Size 2023 To 2032

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The technology which is used in the manufacturing of the autonomous vehicles is artificial intelligence. It happens to be the only technology that provides real time and reliable recognition of various things around the vehicle and due to which the market for artificial intelligence and transportation will grow during the forecast period. The use of this technology helps in providing better optimization in these autonomous cars. Original equipment manufacturers are making use of this technology to provide efficient vehicles.

The existing infrastructure does not support the growth of this technology in the transportation market. And do to which the use of artificial intelligence for transportation becomes difficult. In many regions across the globe artificial intelligence technology is not used in many transport vehicles. Even though the features provided by the use of this technology are great not many vehicles make use of this technology. Most of the vehicles produced today are not supportive of this technology. But in the developed nations the demand for autonomous vehicles has increased to a great extent in order to ensure the safety of the passengers and the driver due to which the incorporation of this technology shall increase.

Manufacturers are constantly engaging on research and development activities in order to integrate the artificial intelligence technology in these regular vehicles. Let's play during the outbreak of the COVID-19 pandemic there was a negative impact on the growth of this market. This industry faced many challenges due to the recession in the market and major disturbance in its operations. The demand for the vehicles had dropped to a great extent. The manufacturing units had also stopped their production due to the unavailability of raw materials and labor. Artificial intelligence is used in the manufacturing of different types of automobiles trucks etc. As the integration of artificial intelligence in the vehicles is a costly affair the demand for it had reduced during the pandemic. All of these factors had a negative impact on the growth of the market.

Growth factors

  • The various regulations which are imposed by the government in order to ensure the vehicle safety
  • The increasing demand for them advanced driver assistance systems across the developed as well as the developing nations
  • Increasing demand for the autonomous vehicles
  • Increase in the partnership among the manufacturers

The growing demand for the use of artificial intelligence technology and transportation market is due to and increased adoption of road safety features across various automobiles. The government initiatives that have mandated a few safety features in the vehicles shall drive the market growth in the coming years. Growing demand for artificial intelligence is due to its use in the autonomous vehicles. In order to reduce the cases of accidents on the road and the fatalities caused by these accidents there's a growing demand for artificial intelligence and transportation. The integration of this technology in the vehicles helps in reducing the human error due to which most of the road accidents are caused. Road accidents happen to be one of the major reasons for death across the globe. Governments in various nations are adopting this technology and creating an awareness regarding its adoption in order to ensure the safety of the passengers and drivers. This technology improves the efficiency of the driver by sailing warnings on the areas that are accident prone and it also provides updates on the important signs and the upcoming roads. The demand for the autonomous cars is maximum in the developed regions and artificial intelligence technology is inbuilt in these cars. Many consumers prefer these vehicles as they provide better safety. The demand for these cars is growing especially in the aging population. The use of artificial technology also helps in predictive maintenance which ensures warning before the breakdown of the car.

Artificial intelligence technology is expected to grow well in the coming years due to growing demand for deep learning which is a combination of artificial intelligence and machine learning. Other reason for the growth of the market is truck platooning. Artificial intelligence will provide more features in the coming years in the transportation segment. Better opportunities for the growth of the market shall be provided by the integration of various features like signal recognition, voice recognition as well as the integration of various sensors that will help in making driving a better experience.

The use of artificial intelligence and transportation we'll see go to growth in the coming years as various governments are adopting policies and laws relating to the reducing of carbon emissions caused by the vehicles. The use of artificial intelligence helps and reducing the consumption of fuel and due to which this technology shall play an important role in reducing the carbon emissions. Initiatives taken by the government of supportive of the technology and these favorable regulations shall drive the market growth in the coming years. Additional assistance provided by the use of this technology in terms of indicating the roads that have less traffic, shorter distance roads etc. shall be other factors that shall increase the demand for this technology in the coming years.

Report Scope of the Artificial Intelligence (AI) in Transportation Market

Report Coverage Details
Market Size in 2023

USD 3.67 Billion

Market Size by 2032

USD 23.11 Billion

Growth Rate from 2023 to 2032 CAGR of 22.70%
Base Year 2022
Forecast Period 2023 to 2032
Segments Covered Offering, Machine Learning Technology, Process, Application, Geography
Companies Mentioned

Volvo, Daimler, Scania, Paccar, Peloton, Valeo, Xevo, ZF, Zonar, Tier-I Suppliers, Software Suppliers, Start-Up’s Bosch, Intel, NVIDIA, Alphabet, Continental, Magna, Man, Microsoft, Nauto, IBM Corporation


Offering Insights

The software segment is expected to dominate the market in the coming years. On the basis of the offering the software segment is expected to have a larger market share in terms of revenue during the forecast period. The segment has dominated the market in the past due to the growing demand for software which happens to be your service platform. Software's are also used in the human machine interface applications of the vehicles. The software segment is expected to grow well in the coming years as it is also providing predictive intelligence for the supply chains. It is used in the waterway shipping, railway transport as well as the air carriers.

Artificial Intelligence in Transportation Market Share, By Offering, 2021 (%)

The use of artificial intelligence helps in managing a few risks by providing solutions. The use of artificial intelligence software's will also be instrumental in reducing the number of breakdowns. Which shall help in reducing the cost of operations and maintenance. The use of this software is also help in a warning or indicating the risks associated with the use of the vehicle in certain conditions. The use of the satellite maps and the digital maps in these systems provide better information about the roads as well as the traffic along the road. The software segment is expected to grow well in the coming years as the demand of the consumers across the globe for the autonomous vehicles is changing continuously.

Application Insights

On the basis of application, the autonomous truck segment is expected to dominate the market in the coming years. The segment had the largest market share in the past and it is expected to grow well in the coming years. The need for these autonomics trucks is expected to grow in the coming years due to the development in the truck industry. There is a growing demand for these drugs for the logistics purposes across various nations in the globe. Trucks play an important role in the transportation of various goods and the demand for them is expected to grow in the coming years.

About 65% transport of the merchandise is done by trucks across the globe. the use of the autonomous trucks shall help in reducing the cost of maintenance and the amount of expenses which are made on these drugs. They shall be reduction of about 45% in these costs. And it shall also be the factor for the growth of the industry in the coming years period.

Artificial Intelligence (AI) in Transportation Market Share, By Region, 2022 (%)

Regions Revenue Share in 2022 (%)
North America 40%
Asia Pacific 21%
Europe 28%
Latin America 7%
MEA 4%


Geography Insights

The North American region is expected to have a dominant marketplace in the coming years. This region is expected to grow well due to the development of various regulations that ensure safety, compliance as well as accountability. The adoption of such regulations that out of favorable for the growth of the market shall create more demand in the coming year. The HOS Revisions will also be one of the factors driving the growth of the artificial intelligence technology in transportation sector. The strong economic condition of the North American region and the higher mount of disposable income shall be the leading factors for the growth of this industry. In order to ensure the safety of the passengers a few features are made mandatory in the manufacturing of the cars. The manufacturers are providing the vehicles with these features due to which the market for artificial intelligence is expected to grow well in the coming years. As these regions have strong financial condition the investments that come in these technologies are high as compared to any other regions across the globe. Constant research and development activities shall also be helpful in providing better features over the coming years. An investment of about U.S. dollar 20 billion was invested in Mexico in the automotive industry.

In the presence of the strong economy along with the good supply chain and logistics sector shall drive the market in the Asia Pacific region. All of these factors shall be instrumental in the growth of the market in the coming years. the Asia Pacific region will also see a good amount of growth in the coming years due to the increased sale of the trucks in this region. The use of artificial intelligence in these trucks will drive the market growth in the coming years. China and Japan have been the major consumers of these trucks in the past. in the Asia Pacific region Japan is projected to make the maximum use of artificial intelligence in the transportation market over the coming years.

Key market developments

  • The major market players have invested an amount of U.S. dollars 20 billion in Mexico for the automotive market. This investment shall be instrumental in the growth of the artificial intelligence and transport for this region.
  • The use of artificial intelligence in the transport sector shall provide high end features in the coming years.

Market Key players

  • Volvo
  • Daimler
  • Scania
  • Paccar
  • Peloton
  • Valeo
  • Xevo
  • ZF
  • Zonar
  • Tier-I Suppliers
  • Software Suppliers
  • Start-Up’s Bosch
  • Intel
  • NVIDIA
  • Alphabet
  • Continental
  • Magna
  • Man
  • Microsoft
  • Nauto
  • IBM Corporation

Segments covered in the report

(Note*: We offer report based on sub segments as well. Kindly, let us know if you are interested)

By Offering

  • Hardware
    • Neuromorphic
    • Von Neumann
  • Software
    • Platforms
    • Solutions

By Machine Learning Technology

  • Deep Learning
  • Computer Vision
  • Context Awareness
  • Natural Language Processing

By Process

  • Signal Recognition
  • Object Recognition
  • Data Mining

By Application

  • Semi Autonomous Truck
  • Truck platooning
  • Predictive maintenance
  • Precision and mapping
  • Autonomous truck
  • Machine human interface
  • Others

By Geography

  • North America
    • U.S.
    • Canada
  • Europe
    • U.K.
    • Germany
    • France
  • Asia-Pacific
    • China
    • India
    • Japan
    • South Korea
    • Malaysia
    • Philippines
  • Latin America
    • Brazil
    • Rest of Latin America
  • Middle East & Africa (MEA)
    • GCC
    • North Africa
    • South Africa
    • Rest of the Middle East & Africa

Frequently Asked Questions

The global artificial intelligence (AI) in transportation market size was accounted at USD 3 billion in 2022 and it is expected to reach around USD 23.11 billion by 2032..

The global artificial intelligence (AI) in transportation market is poised to grow at a CAGR of 22.70% from 2023 to 2032.

The major players operating in the artificial intelligence (AI) in transportation market are Volvo, Daimler, Scania, Paccar, Peloton, Valeo, Xevo, ZF, Zonar, Tier-I Suppliers, Software Suppliers, Start-Up’s Bosch, Intel, NVIDIA, Alphabet, Continental, Magna, Man, Microsoft, Nauto, IBM Corporation

Deep learning is a combination of machine learning and artificial intelligence and the growing demand for deep learning shall drive the market growth in the coming years period

North America region will lead the global artificial intelligence (AI) in transportation market during the forecast period 2023 to 2032.

Chapter 1. Introduction

1.1. Research Objective

1.2. Scope of the Study

1.3. Definition

Chapter 2. Research Methodology

2.1. Research Approach

2.2. Data Sources

2.3. Assumptions & Limitations

Chapter 3. Executive Summary

3.1. Market Snapshot

Chapter 4. Market Variables and Scope 

4.1. Introduction

4.2. Market Classification and Scope

4.3. Industry Value Chain Analysis

4.3.1. Raw Material Procurement Analysis 

4.3.2. Sales and Distribution Channel Analysis

4.3.3. Downstream Buyer Analysis

Chapter 5. COVID 19 Impact on Artificial Intelligence (AI) in Transportation Market 

5.1. COVID-19 Landscape: Artificial Intelligence (AI) in Transportation Industry Impact

5.2. COVID 19 - Impact Assessment for the Industry

5.3. COVID 19 Impact: Global Major Government Policy

5.4. Market Trends and Opportunities in the COVID-19 Landscape

Chapter 6. Market Dynamics Analysis and Trends

6.1. Market Dynamics

6.1.1. Market Drivers

6.1.2. Market Restraints

6.1.3. Market Opportunities

6.2. Porter’s Five Forces Analysis

6.2.1. Bargaining power of suppliers

6.2.2. Bargaining power of buyers

6.2.3. Threat of substitute

6.2.4. Threat of new entrants

6.2.5. Degree of competition

Chapter 7. Competitive Landscape

7.1.1. Company Market Share/Positioning Analysis

7.1.2. Key Strategies Adopted by Players

7.1.3. Vendor Landscape

7.1.3.1. List of Suppliers

7.1.3.2. List of Buyers

Chapter 8. Global Artificial Intelligence (AI) in Transportation Market, By Offering

8.1. Artificial Intelligence (AI) in Transportation Market, by Offering, 2023-2032

8.1.1. Hardware

8.1.1.1. Market Revenue and Forecast (2020-2032)

8.1.2. Software

8.1.2.1. Market Revenue and Forecast (2020-2032)

Chapter 9. Global Artificial Intelligence (AI) in Transportation Market, By Machine Learning Technology

9.1. Artificial Intelligence (AI) in Transportation Market, by Machine Learning Technology e, 2023-2032

9.1.1. Deep Learning

9.1.1.1. Market Revenue and Forecast (2020-2032)

9.1.2. Deep Learning

9.1.2.1. Market Revenue and Forecast (2020-2032)

9.1.3. Context Awareness

9.1.3.1. Market Revenue and Forecast (2020-2032)

9.1.4. Context Awareness

9.1.4.1. Market Revenue and Forecast (2020-2032)

Chapter 10. Global Artificial Intelligence (AI) in Transportation Market, By Process 

10.1. Artificial Intelligence (AI) in Transportation Market, by Process, 2023-2032

10.1.1. Signal Recognition

10.1.1.1. Market Revenue and Forecast (2020-2032)

10.1.2. Object Recognition

10.1.2.1. Market Revenue and Forecast (2020-2032)

10.1.3. Data Mining

10.1.3.1. Market Revenue and Forecast (2020-2032)

Chapter 11. Global Artificial Intelligence (AI) in Transportation Market, By Application 

11.1. Artificial Intelligence (AI) in Transportation Market, by Application, 2023-2032

11.1.1. Semi Autonomous Truck

11.1.1.1. Market Revenue and Forecast (2020-2032)

11.1.2. Truck platooning

11.1.2.1. Market Revenue and Forecast (2020-2032)

11.1.3. Predictive maintenance

11.1.3.1. Market Revenue and Forecast (2020-2032)

11.1.4. Precision and mapping

11.1.4.1. Market Revenue and Forecast (2020-2032)

11.1.5. Autonomous truck

11.1.5.1. Market Revenue and Forecast (2020-2032)

11.1.6. Machine human interface

11.1.6.1. Market Revenue and Forecast (2020-2032)

11.1.7. Others

11.1.7.1. Market Revenue and Forecast (2020-2032)

Chapter 12. Global Artificial Intelligence (AI) in Transportation Market, Regional Estimates and Trend Forecast

12.1. North America

12.1.1. Market Revenue and Forecast, by Offering (2020-2032)

12.1.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.1.3. Market Revenue and Forecast, by Process (2020-2032)

12.1.4. Market Revenue and Forecast, by Application (2020-2032)

12.1.5. U.S.

12.1.5.1. Market Revenue and Forecast, by Offering (2020-2032)

12.1.5.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.1.5.3. Market Revenue and Forecast, by Process (2020-2032)

12.1.5.4. Market Revenue and Forecast, by Application (2020-2032)

12.1.6. Rest of North America

12.1.6.1. Market Revenue and Forecast, by Offering (2020-2032)

12.1.6.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.1.6.3. Market Revenue and Forecast, by Process (2020-2032)

12.1.6.4. Market Revenue and Forecast, by Application (2020-2032)

12.2. Europe

12.2.1. Market Revenue and Forecast, by Offering (2020-2032)

12.2.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.2.3. Market Revenue and Forecast, by Process (2020-2032)

12.2.4. Market Revenue and Forecast, by Application (2020-2032)

12.2.5. UK

12.2.5.1. Market Revenue and Forecast, by Offering (2020-2032)

12.2.5.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.2.5.3. Market Revenue and Forecast, by Process (2020-2032)

12.2.5.4. Market Revenue and Forecast, by Application (2020-2032)

12.2.6. Germany

12.2.6.1. Market Revenue and Forecast, by Offering (2020-2032)

12.2.6.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.2.6.3. Market Revenue and Forecast, by Process (2020-2032)

12.2.6.4. Market Revenue and Forecast, by Application (2020-2032)

12.2.7. France

12.2.7.1. Market Revenue and Forecast, by Offering (2020-2032)

12.2.7.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.2.7.3. Market Revenue and Forecast, by Process (2020-2032)

12.2.7.4. Market Revenue and Forecast, by Application (2020-2032)

12.2.8. Rest of Europe

12.2.8.1. Market Revenue and Forecast, by Offering (2020-2032)

12.2.8.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.2.8.3. Market Revenue and Forecast, by Process (2020-2032)

12.2.8.4. Market Revenue and Forecast, by Application (2020-2032)

12.3. APAC

12.3.1. Market Revenue and Forecast, by Offering (2020-2032)

12.3.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.3.3. Market Revenue and Forecast, by Process (2020-2032)

12.3.4. Market Revenue and Forecast, by Application (2020-2032)

12.3.5. India

12.3.5.1. Market Revenue and Forecast, by Offering (2020-2032)

12.3.5.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.3.5.3. Market Revenue and Forecast, by Process (2020-2032)

12.3.5.4. Market Revenue and Forecast, by Application (2020-2032)

12.3.6. China

12.3.6.1. Market Revenue and Forecast, by Offering (2020-2032)

12.3.6.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.3.6.3. Market Revenue and Forecast, by Process (2020-2032)

12.3.6.4. Market Revenue and Forecast, by Application (2020-2032)

12.3.7. Japan

12.3.7.1. Market Revenue and Forecast, by Offering (2020-2032)

12.3.7.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.3.7.3. Market Revenue and Forecast, by Process (2020-2032)

12.3.7.4. Market Revenue and Forecast, by Application (2020-2032)

12.3.8. Rest of APAC

12.3.8.1. Market Revenue and Forecast, by Offering (2020-2032)

12.3.8.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.3.8.3. Market Revenue and Forecast, by Process (2020-2032)

12.3.8.4. Market Revenue and Forecast, by Application (2020-2032)

12.4. MEA

12.4.1. Market Revenue and Forecast, by Offering (2020-2032)

12.4.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.4.3. Market Revenue and Forecast, by Process (2020-2032)

12.4.4. Market Revenue and Forecast, by Application (2020-2032)

12.4.5. GCC

12.4.5.1. Market Revenue and Forecast, by Offering (2020-2032)

12.4.5.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.4.5.3. Market Revenue and Forecast, by Process (2020-2032)

12.4.5.4. Market Revenue and Forecast, by Application (2020-2032)

12.4.6. North Africa

12.4.6.1. Market Revenue and Forecast, by Offering (2020-2032)

12.4.6.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.4.6.3. Market Revenue and Forecast, by Process (2020-2032)

12.4.6.4. Market Revenue and Forecast, by Application (2020-2032)

12.4.7. South Africa

12.4.7.1. Market Revenue and Forecast, by Offering (2020-2032)

12.4.7.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.4.7.3. Market Revenue and Forecast, by Process (2020-2032)

12.4.7.4. Market Revenue and Forecast, by Application (2020-2032)

12.4.8. Rest of MEA

12.4.8.1. Market Revenue and Forecast, by Offering (2020-2032)

12.4.8.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.4.8.3. Market Revenue and Forecast, by Process (2020-2032)

12.4.8.4. Market Revenue and Forecast, by Application (2020-2032)

12.5. Latin America

12.5.1. Market Revenue and Forecast, by Offering (2020-2032)

12.5.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.5.3. Market Revenue and Forecast, by Process (2020-2032)

12.5.4. Market Revenue and Forecast, by Application (2020-2032)

12.5.5. Brazil

12.5.5.1. Market Revenue and Forecast, by Offering (2020-2032)

12.5.5.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.5.5.3. Market Revenue and Forecast, by Process (2020-2032)

12.5.5.4. Market Revenue and Forecast, by Application (2020-2032)

12.5.6. Rest of LATAM

12.5.6.1. Market Revenue and Forecast, by Offering (2020-2032)

12.5.6.2. Market Revenue and Forecast, by Machine Learning Technology (2020-2032)

12.5.6.3. Market Revenue and Forecast, by Process (2020-2032)

12.5.6.4. Market Revenue and Forecast, by Application (2020-2032)

Chapter 13. Company Profiles

13.1. Volvo

13.1.1. Company Overview

13.1.2. Product Offerings

13.1.3. Financial Performance

13.1.4. Recent Initiatives

13.2. Daimler

13.2.1. Company Overview

13.2.2. Product Offerings

13.2.3. Financial Performance

13.2.4. Recent Initiatives

13.3. Scania

13.3.1. Company Overview

13.3.2. Product Offerings

13.3.3. Financial Performance

13.3.4. Recent Initiatives

13.4. Paccar

13.4.1. Company Overview

13.4.2. Product Offerings

13.4.3. Financial Performance

13.4.4. Recent Initiatives

13.5. Peloton

13.5.1. Company Overview

13.5.2. Product Offerings

13.5.3. Financial Performance

13.5.4. Recent Initiatives

13.6. Valeo

13.6.1. Company Overview

13.6.2. Product Offerings

13.6.3. Financial Performance

13.6.4. Recent Initiatives

13.7. Xevo

13.7.1. Company Overview

13.7.2. Product Offerings

13.7.3. Financial Performance

13.7.4. Recent Initiatives

13.8. ZF

13.8.1. Company Overview

13.8.2. Product Offerings

13.8.3. Financial Performance

13.8.4. Recent Initiatives

13.9. Zonar

13.9.1. Company Overview

13.9.2. Product Offerings

13.9.3. Financial Performance

13.9.4. Recent Initiatives

13.10. Tier-I Suppliers

13.10.1. Company Overview

13.10.2. Product Offerings

13.10.3. Financial Performance

13.10.4. Recent Initiatives

Chapter 14. Research Methodology

14.1. Primary Research

14.2. Secondary Research

14.3. Assumptions

Chapter 15. Appendix

15.1. About Us

15.2. Glossary of Terms

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