모빌리티용 AI 시장 : 모빌리티 유형별, 기술별, 배포 모드별, 용도별, 최종사용자별 - 세계 예측(2025-2030년)

The AI in Mobility Market was valued at USD 9.90 billion in 2024 and is projected to grow to USD 11.41 billion in 2025, with a CAGR of 15.60%, reaching USD 23.63 billion by 2030.

An introduction to how artificial intelligence is revolutionizing mobility ecosystems to improve efficiency safety and reliability in mobility systems

The integration of artificial intelligence in mobility is driving a paradigm shift across transportation ecosystems, unlocking unprecedented levels of performance, safety, and operational excellence. By leveraging sophisticated algorithms and real-time data, organizations can anticipate demand, optimize routing, and reduce downtime. This introduction examines the scope and objectives of the study, providing a foundational understanding of how AI innovations are influencing air, land, and maritime mobility.

Through a methodical exploration of technological advancements, regulatory influences, and industry initiatives, this section lays the groundwork for the subsequent analysis. It outlines the core research questions, the key areas of focus, and the intended audience, ensuring that stakeholders gain clear insights into the evolving role of AI in transforming passenger experiences and freight movement globally.

Revealing transformative shifts driven by advanced AI technologies converging with mobility operations to disrupt transport models and enable smarter journeys

Advancements in computer vision, sensor fusion, and machine learning are reshaping the very fabric of mobility operations. Predictive analytics now forecast maintenance needs before failures occur, while natural language processing powers intuitive voice interfaces for drivers and passengers. These technologies converge to redefine the way vehicles interact with environments and operators, enabling seamless data exchange across air, land, and maritime domains.

As these tools mature, they facilitate real-time decision making in dynamic conditions, reducing human error and enhancing responsiveness. Moreover, the growing integration of AI with Internet of Things platforms and cloud infrastructures is fostering new models of cross-modal coordination. By examining these transformative shifts, stakeholders can better appreciate how AI is driving smarter, safer journeys and unlocking fresh opportunities in mobility ecosystems.

Assessing how evolving United States trade duties impact mobility supply chains manufacturing costs and cross border transportation networks

Recent adjustments in United States trade duties have introduced new cost structures and logistical complexities for mobility manufacturers and service providers. Components sourced from affected regions now incur higher tariffs, prompting supply chain realignments and sourcing diversification. As a result, prototype development and large-scale deployments face evolving budgetary considerations and extended lead times.

In response to these trade duty changes, manufacturers are exploring strategic partnerships and nearshoring options to mitigate cost pressures. This section assesses how these evolving trade duties ripple through production networks, influence material procurement decisions, and shape long-term planning for global transportation projects.

Deep insights revealing how segmentation by mobility type technology deployment mode application and end user category guides innovation and investment choices

The market's first axis of segmentation examines mobility types, distinguishing air, land, and maritime submarkets with rail and road transport as key subcategories. Each segment exhibits distinct operational challenges and regulatory frameworks, influencing how AI solutions are tailored for specific vehicle classes and infrastructure requirements.

A second segmentation layer focuses on core technologies, encompassing computer vision with image recognition, object detection, and video analytics; machine learning variants including supervised, unsupervised, and reinforcement learning; natural language processing with speech recognition and text analytics; and multi-level sensor fusion integrating data, feature, and decision insights. These frameworks form the technological foundation for innovation across deployment modes, which can be delivered via private or public cloud environments or on-premise architectures to meet diverse security and performance requirements.

Applications form the next segmentation domain, spanning advanced driver assistance systems with adaptive cruise control and blind spot detection, through autonomous driving, fleet management including driver behavior monitoring and fuel management, route optimization with dynamic routing capabilities, predictive maintenance, and telematics solutions. Finally, end user segmentation highlights commercial operators such as logistics companies and mobility service providers, governments and municipalities shaping public transit systems, and passenger use cases from individual ownership to ride-hailing services. Altogether, these multi-tiered perspectives guide stakeholders in prioritizing investment and innovation efforts.

Illuminating how regional dynamics across Americas Europe Middle East Africa and Asia Pacific shape adoption trends and investment strategies in AI mobility

Regional dynamics play a pivotal role in shaping the pace and nature of AI adoption within mobility markets. In the Americas, investment momentum is driven by robust infrastructure funding and a strong focus on autonomous vehicle pilots. Meanwhile, Europe, Middle East, and Africa regions emphasize regulatory compliance and data privacy standards as they integrate AI into public transit and smart city initiatives.

Across Asia Pacific, rapid urbanization and government-led innovation programs are accelerating deployments of AI enabled solutions in both passenger and freight segments. Divergent regulatory landscapes and infrastructure readiness levels in each region influence strategic partnerships, public-private collaborations, and adoption curves. Recognizing these nuances allows industry participants to tailor market entry strategies and leverage regional strengths effectively.

Analysis of leading companies shaping the competitive landscape through strategic collaborations and driving innovation in AI mobility solutions

Leading technology providers and tier-one automotive OEMs are forging strategic partnerships to advance AI driven mobility platforms. Collaborations between software innovators and component manufacturers are streamlining end-to-end system integration, accelerating time to market for advanced driver assistance and autonomous driving modules.

Startups specializing in sensor fusion and computer vision are securing funding from venture capital and corporate investors, challenging incumbents to bolster in-house R&D and pursue targeted acquisitions. This competitive interplay fosters an ecosystem where agility and scale converge, driving continuous refinement of AI algorithms and deployment frameworks across global mobility networks.

Actionable recommendations for industry leaders to harness emerging AI mobility trends optimize investments and accelerate transportation innovation

Industry leaders should prioritize cross-functional collaboration between AI specialists, vehicle engineers, and operations teams to ensure seamless integration of intelligent systems. Establishing pilot programs with clear performance metrics can validate technology efficacy while minimizing operational risks. Investing in scalable data architectures and edge computing capabilities will facilitate real-time processing and support future feature expansions.

Engaging proactively with regulatory bodies and standard-setting organizations is essential to influence policy frameworks and ensure compliance. Cultivating talent through partnerships with academic institutions and specialized training programs will address skill gaps and foster a culture of continuous innovation. By executing these strategic recommendations, organizations can capitalize on emerging trends and secure competitive advantage in the evolving mobility landscape.

A detailed explanation of the rigorous research methodology used to collect and analyze industry data ensure insight validity and underpin AI mobility findings

A combination of secondary research and expert interviews underpins the report's investigative rigor. Publicly available industry publications, patent filings, and regulatory documents provided a foundational knowledge base. These insights were complemented by primary discussions with executives, engineers, and analysts across technology vendors, vehicle OEMs, and service operators.

Quantitative data sets were meticulously validated through triangulation, correlating multiple sources to ensure consistency and accuracy. Qualitative findings underwent peer review by subject matter experts, further enhancing insight credibility. This robust methodology guarantees that the resulting market intelligence reflects the latest developments and supports informed decision making.

Synthesizing critical AI mobility insights underscoring the impact on future transportation paradigms and charting a strategic path forward for stakeholders

The insights presented in this report converge to illustrate the profound transformation underway in transportation ecosystems. Artificial intelligence is catalyzing new levels of automation, safety, and efficiency, fundamentally redefining how people and goods move around the globe. Stakeholders who embrace these advancements will unlock fresh revenue streams and operational improvements.

As mobility markets continue to evolve, collaboration across technology developers, infrastructure providers, and regulatory authorities will be essential. By synthesizing the critical findings and charting a clear strategic path, this conclusion equips decision makers with the perspective needed to navigate future challenges and seize emerging opportunities in AI driven mobility.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

• 4.1. Introduction
• 4.2. Market Sizing & Forecasting

5. Market Dynamics

• 5.1. Implementation of AI-powered dynamic parking management systems reducing urban congestion and emissions
• 5.2. AI-powered multimodal journey planners integrating real-time weather and transit accessibility data
• 5.3. Smart in-cabin monitoring solutions using computer vision and sentiment analysis to enhance passenger safety
• 5.4. Autonomous last-mile delivery robots integrating advanced computer vision and lidar for urban logistics
• 5.5. Adaptive traffic management systems leveraging federated learning to optimize urban mobility networks
• 5.6. Edge AI-enabled predictive maintenance platforms reducing downtime in electric bus fleets
• 5.7. Generative AI-driven digital twin platforms for simulating and optimizing electric vehicle charging infrastructure
• 5.8. Computer vision and sentiment analysis powering smart in-cabin monitoring systems to enhance passenger safety and comfort
• 5.9. AI-enabled journey planning platforms integrating real-time weather forecasts and transit accessibility for optimized multimodal itineraries
• 5.10. AI-driven predictive passenger flow modeling to dynamically adjust train frequencies

6. Market Insights

• 6.1. Porter's Five Forces Analysis
• 6.2. PESTLE Analysis

7. Cumulative Impact of United States Tariffs 2025

8. AI in Mobility Market, by Mobility Type

• 8.1. Introduction
• 8.2. Air Mobility
• 8.3. Land Mobility
  • 8.3.1. Rail Transport
  • 8.3.2. Road Transport
• 8.4. Maritime Mobility

9. AI in Mobility Market, by Technology

• 9.1. Introduction
• 9.2. Computer Vision
  • 9.2.1. Image Recognition
  • 9.2.2. Object Detection
  • 9.2.3. Video Analytics
• 9.3. Machine Learning
  • 9.3.1. Reinforcement Learning
  • 9.3.2. Supervised Learning
  • 9.3.3. Unsupervised Learning
• 9.4. Natural Language Processing
  • 9.4.1. Speech Recognition
  • 9.4.2. Text Analytics
• 9.5. Sensor Fusion
  • 9.5.1. Data-level Fusion
  • 9.5.2. Decision-level Fusion
  • 9.5.3. Feature-level Fusion

10. AI in Mobility Market, by Deployment Mode

• 10.1. Introduction
• 10.2. Cloud
  • 10.2.1. Private Cloud
  • 10.2.2. Public Cloud
• 10.3. On-Premise

11. AI in Mobility Market, by Application

• 11.1. Introduction
• 11.2. Advanced Driver Assistance Systems
  • 11.2.1. Adaptive Cruise Control
  • 11.2.2. Automatic Emergency Braking
  • 11.2.3. Blind Spot Detection
  • 11.2.4. Lane Departure Warning
• 11.3. Autonomous Driving
• 11.4. Fleet Management
  • 11.4.1. Driver Behavior Monitoring
  • 11.4.2. Fuel Management
  • 11.4.3. Vehicle Tracking
• 11.5. Predictive Maintenance
• 11.6. Route Optimization
  • 11.6.1. Dynamic Routing
  • 11.6.2. Real-time Traffic
• 11.7. Telematics
  • 11.7.1. GPRS/Cellular
  • 11.7.2. On-board Diagnostics

12. AI in Mobility Market, by End User

• 12.1. Introduction
• 12.2. Commercial
  • 12.2.1. Logistics Companies
  • 12.2.2. Mobility Service Providers
• 12.3. Governments & Municipalities
• 12.4. Passenger
  • 12.4.1. Individual
  • 12.4.2. Ride-hailing

13. Americas AI in Mobility Market

• 13.1. Introduction
• 13.2. United States
• 13.3. Canada
• 13.4. Mexico
• 13.5. Brazil
• 13.6. Argentina

14. Europe, Middle East & Africa AI in Mobility Market

• 14.1. Introduction
• 14.2. United Kingdom
• 14.3. Germany
• 14.4. France
• 14.5. Russia
• 14.6. Italy
• 14.7. Spain
• 14.8. United Arab Emirates
• 14.9. Saudi Arabia
• 14.10. South Africa
• 14.11. Denmark
• 14.12. Netherlands
• 14.13. Qatar
• 14.14. Finland
• 14.15. Sweden
• 14.16. Nigeria
• 14.17. Egypt
• 14.18. Turkey
• 14.19. Israel
• 14.20. Norway
• 14.21. Poland
• 14.22. Switzerland

15. Asia-Pacific AI in Mobility Market

• 15.1. Introduction
• 15.2. China
• 15.3. India
• 15.4. Japan
• 15.5. Australia
• 15.6. South Korea
• 15.7. Indonesia
• 15.8. Thailand
• 15.9. Philippines
• 15.10. Malaysia
• 15.11. Singapore
• 15.12. Vietnam
• 15.13. Taiwan

16. Competitive Landscape

• 16.1. Market Share Analysis, 2024
• 16.2. FPNV Positioning Matrix, 2024
• 16.3. Competitive Analysis
  • 16.3.1. NVIDIA Corporation
  • 16.3.2. International Business Machines Corporation
  • 16.3.3. AB Volvo
  • 16.3.4. Aurora Innovation, Inc.
  • 16.3.5. BMW AG
  • 16.3.6. Continental AG
  • 16.3.7. Denso Corporation
  • 16.3.8. Ford Motor Company
  • 16.3.9. General Motors Holdings LLC
  • 16.3.10. Intel Corporation
  • 16.3.11. Magna International Inc.
  • 16.3.12. Microsoft Corporation
  • 16.3.13. Ouster Inc.
  • 16.3.14. Qualcomm Incorporated
  • 16.3.15. Renesas Electronics Corporation
  • 16.3.16. Robert Bosch GmbH
  • 16.3.17. Scania AB
  • 16.3.18. Tesla, Inc.
  • 16.3.19. Uber Technologies, Inc.
  • 16.3.20. Valeo SA
  • 16.3.21. Volkswagen AG
  • 16.3.22. Xpeng Inc.
  • 16.3.23. ZF Friedrichshafen AG

17. ResearchAI

18. ResearchStatistics

19. ResearchContacts

20. ResearchArticles

21. Appendix