세계의 자율주행 시장 : 컴포넌트별, 자율주행 레벨별, 차종별, 추진 유형별, 차량 용도별, 지역별 - 시장 규모, 산업 역학, 기회 분석, 예측(2025-2033년)

Today, the autonomous driving market is on a strong upward trajectory, propelled by rapid technological advancements and increasing consumer confidence in self-driving systems. In 2024, the market was valued at approximately US$170.22 billion and is projected to grow substantially, reaching a valuation of US$668.64 billion by 2033. This impressive expansion corresponds to a compound annual growth rate (CAGR) of 17.63% during the forecast period from 2025 to 2033, highlighting the accelerating pace of innovation and adoption within the autonomous driving sector.

Regionally, the global autonomous driving market is witnessing notable growth patterns, with the Asia Pacific region projected to emerge as the largest market, closely followed by North America. Asia Pacific's dominance is fueled by a combination of supportive government initiatives, rapid technological progress, and the strong presence of leading automakers in the region. Although North America currently holds the largest share of the market, Asia Pacific is expected to experience faster growth, driven by proactive measures and investments. China, in particular, is aggressively promoting the development of autonomous vehicles through substantial government backing, widespread testing programs, and the deployment of robotaxi services.

Noteworthy Market Developments

The autonomous driving market is characterized by intense competition between established automakers and leading technology giants, each following unique technological strategies to capture market share. Tesla remains a dominant force, but Waymo is a close contender, maintaining a leadership position with a fleet of over 700 vehicles actively operating in key U.S. cities such as Phoenix, San Francisco, and Los Angeles. By mid-2024, Waymo's autonomous vehicles were completing more than 150,000 paid rides every week, demonstrating both the scale and robustness of its service.

The competitive landscape also reveals a variety of distinct approaches toward market penetration and the development of autonomous technologies. For example, Apple's Project Titan, initially rumored to pursue full autonomy, has since shifted focus. Although the company has scaled back its ambitions for a fully autonomous vehicle, it continues to invest heavily in advanced driver assistance systems (ADAS). These systems aim to enhance vehicle safety and convenience and are expected to reach the market around 2028. Apple's more cautious and incremental approach reflects a broader trend in the industry where companies balance innovation with regulatory challenges and market readiness.

Core Growth Drivers

The autonomous driving market is progressing at an impressive pace, driven largely by strategic collaborations between traditional automakers and cutting-edge technology companies that are collectively reshaping the landscape of the industry. These partnerships enable the combination of automotive manufacturing expertise with advanced artificial intelligence and sensor technologies, accelerating the development and deployment of autonomous vehicles. A notable example occurred in January 2024, when Uber announced a partnership with Wayve to launch fully driverless robotaxi trials in London by 2026. This collaboration leverages Wayve's Embodied AI technology, which is designed to seamlessly integrate autonomous driving capabilities into Uber's extensive network that facilitates around 125,000 rides daily. The initiative represents a significant step toward commercializing driverless mobility services in one of the world's largest urban markets.

Emerging Opportunity Trends

The autonomous driving market is experiencing a profound transformation as it shifts toward shared mobility, with robotaxis playing a pivotal role in reshaping urban transportation. These autonomous ride-hailing services are becoming a key innovation driver, offering a glimpse into the future of city travel. Projections suggest that by 2030, approximately 2.5 million robotaxis will be operational around the world, covering more than 200 cities globally. This anticipated expansion reflects both technological progress and increasing public acceptance of autonomous shared mobility as a viable and efficient alternative to traditional transportation.

Barriers to Optimization

Despite rapid advancements in autonomous driving technology, the market continues to grapple with significant challenges in earning public trust, largely due to widespread skepticism and concerns over privacy. Many consumers remain cautious about embracing autonomous vehicles, fearing potential risks associated with the technology. This apprehension has been fueled by several high-profile cybersecurity incidents that have exposed vulnerabilities within connected vehicle systems. For instance, the Nissan Connect EV program suffered a notable breach, raising alarms about the possible exploitation of vehicle software. Additionally, Fiat Chrysler was compelled to recall 1.4 million vehicles due to identified software vulnerabilities, underscoring the tangible risks that software flaws can pose to vehicle safety and security.

Detailed Market Segmentation

By Component, in the autonomous driving market, hardware components hold a commanding position, accounting for more than 65% of the market share. This dominance reflects the critical importance of physical sensors and computing infrastructure in enabling autonomous vehicle functionality. The development and deployment of sophisticated sensor technology require substantial investment, as these components form the foundational elements that allow vehicles to perceive and interpret their surroundings accurately.

By Autonomous Level, vehicles classified as Level 0, which have no driving automation, hold a substantial 43.63% share. This prevalence is largely a reflection of current economic realities and infrastructural challenges. Most vehicles currently on the road are, on average, 12.5 years old, a period that predates the widespread introduction of autonomous driving technologies. As a result, the majority of the existing fleet lacks the hardware and software capabilities necessary to support any level of driving automation. This explains why Level 0 vehicles continue to dominate the market despite growing interest in autonomous technologies.

By Vehicle Type, in the autonomous driving market, SUVs hold a prominent position, capturing approximately 34.20% of the market share. This strong presence is largely due to the inherent advantages SUVs offer as platforms for integrating advanced sensor technologies essential for autonomous operation. One key benefit of SUVs is their elevated mounting positions, which allow LiDAR and camera systems to achieve a significantly improved field of view, typically enhanced by 25 to 35 degrees compared to traditional sedans.

By Propulsion Type, in the autonomous driving market, electric vehicles (EVs) hold a significant advantage, commanding over 45.36% of the market share. This dominance is largely due to the natural technological synergies between electric drivetrains and autonomous systems. Electric vehicles are particularly well-suited to support the energy demands of autonomous hardware, which requires substantial and sustained power. The high-voltage architectures found in EVs can efficiently manage continuous computing power ranging from 3,000 to 5,000 watts, enabling advanced autonomous functions without compromising the vehicle's drivetrain efficiency.

Segment Breakdown

By Component

• Hardware
  • LiDAR (Light Detection and Ranging) Sensors
  • Cameras
  • RADAR (Radio Detection and Ranging) Sensors
  • Ultrasonic Sensors
  • GPS and IMU (Inertial Measurement Unit)
  • ECUs (Electronic Control Units)
  • Connectivity Modules (V2X, 5G)
• Software
  • Solutions
    • AI Algorithms (Machine Learning, Deep Learning)
    • Mapping & Localization Software
    • Sensor Fusion Algorithms
    • Path Planning & Control Software
    • Cybersecurity Solutions
  • Services
    • Professional
    • Integration Services
    • Consulting Services
    • Customization & Development
    • Managed
    • Remote Monitoring & Diagnostics
    • Software Updates & Patches
    • Fleet Management
    • Data Storage & Management

By Autonomous Level

• Level 0: no driving automation
• Level 1: driver assistance
• Level 2: partial driving automation
• Level 3: conditional driving automation
• Level 4: high driving automation
• Level 5: full driving automation

By Vehicle Type

• Sedans
• SUVs
• Buses
• Truck
• Tractor
• Others

By Propulsion Type

• Internal Combustion Engine (ICE) Vehicles
• Electric Vehicles (EVs)
• Hybrid Vehicles

By Vehicle Application

• Passenger/Private Vehicles
• Commercial Vehicles
  • Ride Hailing
  • Public Transport
    • Autonomous Buses & Shuttles
    • AI-Based Route Optimization for Mass Transit
  • Logistics
    • Autonomous Freight Trucks & Delivery Vans
    • AI-Powered Last-Mile Delivery Vehicles
    • Warehouse & Distribution Center Autonomous Fleets
• Heavy/Off-road Vehicles
  • Mining
  • Warehouse
  • Others

By Region

• North America
  • The U.S.
  • Canada
  • Mexico
• Europe
  • The UK
  • Germany
  • France
  • Italy
  • Spain
  • Poland
  • Russia
  • Rest of Europe
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Australia & New Zealand
  • ASEAN
    • Malaysia
    • Singapore
    • Thailand
    • Indonesia
    • Philippines
    • Vietnam
    • Rest of ASEAN
  • Rest of Asia Pacific
• Middle East & Africa
  • UAE
  • Saudi Arabia
  • South Africa
  • Rest of MEA
• South America
  • Argentina
  • Brazil
  • Rest of South America

Leading Market Participants

• NVIDIA Corporation
• IPG Automotive GmbH
• KPIT Technologies Ltd
• Waymo LLC
• Aptiv PLC
• Infineon Technologies AG
• Motional, Inc .
• Tesla Inc.
• Other Prominent Players

Table of Content

Chapter 1. Research Framework

• 1.1. Research Objective
• 1.2. Product Overview
• 1.3. Market Segmentation

Chapter 2. Research Methodology

• 2.1. Qualitative Research
  • 2.1.1. Primary & Secondary Sources
• 2.2. Quantitative Research
  • 2.2.1. Primary & Secondary Sources
• 2.3. Breakdown of Primary Research Respondents, By Region
• 2.4. Assumption for the Study
• 2.5. Market Size Estimation
• 2.6. Data Triangulation

Chapter 3. Executive Summary: Global Autonomous Driving Market

Chapter 4. Global Autonomous Driving Market Overview

• 4.1. Industry Value Chain Analysis
  • 4.1.1. Service Provider
  • 4.1.2. End User
• 4.2. Industry Outlook
  • 4.2.1. Overview of Advanced Driving Assistance System (ADAS)
  • 4.2.2. Overview of Autonomous Vehicles
• 4.3. PESTLE Analysis
• 4.4. Porter's Five Forces Analysis
  • 4.4.1. Bargaining Power of Suppliers
  • 4.4.2. Bargaining Power of Buyers
  • 4.4.3. Threat of Substitutes
  • 4.4.4. Threat of New Entrants
  • 4.4.5. Degree of Competition
• 4.5. Market Dynamics and Trends
  • 4.5.1. Growth Drivers
  • 4.5.2. Restraints
  • 4.5.3. Opportunities
  • 4.5.4. Key Trends
• 4.6. Market Growth and Outlook
  • 4.6.1. Market Revenue Estimates and Forecast (US$ Bn), 2020-2033
  • 4.6.2. Price Trend Analysis
    • 4.6.2.1. By Vehicle Type
    • 4.6.2.2. By Propulsion
    • 4.6.2.3. By Automation Level
• 4.7. Competition Dashboard
  • 4.7.1. Market Concentration Rate
  • 4.7.2. Company Market Share Analysis (Value %), 2024
  • 4.7.3. Competitor Mapping & Benchmarking
• 4.8. Actionable Insights (Analyst's Recommendations)

Chapter 5. Global Autonomous Driving Market Analysis, By Component

• 5.1. Key Insights
• 5.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 5.2.1. Hardware
    • 5.2.1.1. LiDAR (Light Detection and Ranging) Sensors
    • 5.2.1.2. Cameras
    • 5.2.1.3. RADAR (Radio Detection and Ranging) Sensors
    • 5.2.1.4. Ultrasonic Sensors
    • 5.2.1.5. GPS and IMU (Inertial Measurement Unit)
    • 5.2.1.6. ECUs (Electronic Control Units)
    • 5.2.1.7. Connectivity Modules (V2X, 5G)
  • 5.2.2. Software
    • 5.2.2.1. Solutions
      • 5.2.2.1.1. AI Algorithms (Machine Learning, Deep Learning)
      • 5.2.2.1.2. Mapping & Localization Software
      • 5.2.2.1.3. Sensor Fusion Algorithms
      • 5.2.2.1.4. Path Planning & Control Software
      • 5.2.2.1.5. Cybersecurity Solutions
    • 5.2.2.2. Services
      • 5.2.2.2.1. Professional
        • 5.2.2.2.1.1. Integration Services
        • 5.2.2.2.1.2. Consulting Services
        • 5.2.2.2.1.3. Customization & Development
      • 5.2.2.2.2. Managed
        • 5.2.2.2.2.1. Remote Monitoring & Diagnostics
        • 5.2.2.2.2.2. Software Updates & Patches
        • 5.2.2.2.2.3. Fleet Management
        • 5.2.2.2.2.4. Data Storage & Management

Chapter 6. Global Autonomous Driving Market Analysis, By Autonomous Level

• 6.1. Key Insights
• 6.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 6.2.1. Level 0: no driving automation
  • 6.2.2. Level 1: driver assistance
  • 6.2.3. Level 2: partial driving automation
  • 6.2.4. Level 3: conditional driving automation
  • 6.2.5. Level 4: high driving automation
  • 6.2.6. Level 5: full driving automation

Chapter 7. Global Autonomous Driving Market Analysis, By Vehicle Type

• 7.1. Key Insights
• 7.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 7.2.1. Sedans
  • 7.2.2. SUVs
  • 7.2.3. Buses
  • 7.2.4. Truck
  • 7.2.5. Tractor
  • 7.2.6. Others

Chapter 8. Global Autonomous Driving Market Analysis, By Propulsion Type

• 8.1. Key Insights
• 8.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 8.2.1. Internal Combustion Engine (ICE) Vehicles
  • 8.2.2. Electric Vehicles (EVs)
  • 8.2.3. Hybrid Vehicles

Chapter 9. Global Autonomous Driving Market Analysis, By Vehicle Application

• 9.1. Key Insights
• 9.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 9.2.1. Passenger/Private Vehicles
  • 9.2.2. Commercial Vehicles
    • 9.2.2.1. Ride Hailing
    • 9.2.2.2. Public Transport
      • 9.2.2.2.1. Autonomous Buses & Shuttles
      • 9.2.2.2.2. AI-Based Route Optimization for Mass Transit
    • 9.2.2.3. Logistics
      • 9.2.2.3.1. Autonomous Freight Trucks & Delivery Vans
      • 9.2.2.3.2. AI-Powered Last-Mile Delivery Vehicles
      • 9.2.2.3.3. Warehouse & Distribution Center Autonomous Fleets
  • 9.2.3. Heavy/Off-road Vehicles
    • 9.2.3.1. Mining
    • 9.2.3.2. Warehouse
    • 9.2.3.3. Others

Chapter 10. Global Autonomous Driving Market Analysis, By Region

• 10.1. Key Insights
• 10.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 10.2.1. North America
    • 10.2.1.1. The U.S.
    • 10.2.1.2. Canada
    • 10.2.1.3. Mexico
  • 10.2.2. Western Europe
    • 10.2.2.1. The UK
    • 10.2.2.2. Germany
    • 10.2.2.3. France
    • 10.2.2.4. Italy
    • 10.2.2.5. Spain
    • 10.2.2.6. Rest of Western Europe
  • 10.2.3. Eastern Europe
    • 10.2.3.1. Poland
    • 10.2.3.2. Russia
    • 10.2.3.3. Hungary
    • 10.2.3.4. Rest of Eastern Europe
  • 10.2.4. Asia Pacific
    • 10.2.4.1. China
    • 10.2.4.2. India
    • 10.2.4.3. Japan
    • 10.2.4.4. South Korea
    • 10.2.4.5. Australia & New Zealand
    • 10.2.4.6. ASEAN
    • 10.2.4.7. Rest of Asia Pacific
  • 10.2.5. Middle East
    • 10.2.5.1. UAE
    • 10.2.5.2. Saudi Arabia
    • 10.2.5.3. Bahrain
    • 10.2.5.4. Kuwait
    • 10.2.5.5. Qatar
    • 10.2.5.6. Rest of Middle East
  • 10.2.6. Africa
    • 10.2.6.1. Morocco
    • 10.2.6.2. Egypt
    • 10.2.6.3. Nigeria
    • 10.2.6.4. South Africa
    • 10.2.6.5. Rest of Africa
  • 10.2.7. South America
    • 10.2.7.1. Argentina
    • 10.2.7.2. Brazil
    • 10.2.7.3. Rest of South America

Chapter 11. North America Autonomous Driving Market Analysis

• 11.1. Key Insights
• 11.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 11.2.1. By Component
  • 11.2.2. By Autonomous Level
  • 11.2.3. By Vehicle Type
  • 11.2.4. By Propulsion Type
  • 11.2.5. By Vehicle Application
  • 11.2.6. By Country

Chapter 12. Western Europe Autonomous Driving Market Analysis

• 12.1. Key Insights
• 12.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 12.2.1. By Component
  • 12.2.2. By Autonomous Level
  • 12.2.3. By Vehicle Type
  • 12.2.4. By Propulsion Type
  • 12.2.5. By Vehicle Application
  • 12.2.6. By Country

Chapter 13. Eastern Europe Autonomous Driving Market Analysis

• 13.1. Key Insights
• 13.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 13.2.1. By Component
  • 13.2.2. By Autonomous Level
  • 13.2.3. By Vehicle Type
  • 13.2.4. By Propulsion Type
  • 13.2.5. By Vehicle Application
  • 13.2.6. By Country

Chapter 14. Asia Pacific Autonomous Driving Market Analysis

• 14.1. Key Insights
• 14.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 14.2.1. By Component
  • 14.2.2. By Autonomous Level
  • 14.2.3. By Vehicle Type
  • 14.2.4. By Propulsion Type
  • 14.2.5. By Vehicle Application
  • 14.2.6. By Country

Chapter 15. Middle East Autonomous Driving Market Analysis

• 15.1. Key Insights
• 15.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 15.2.1. By Component
  • 15.2.2. By Autonomous Level
  • 15.2.3. By Vehicle Type
  • 15.2.4. By Propulsion Type
  • 15.2.5. By Vehicle Application
  • 15.2.6. By Country

Chapter 16. Africa Autonomous Driving Market Analysis

• 16.1. Key Insights
• 16.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 16.2.1. By Component
  • 16.2.2. By Autonomous Level
  • 16.2.3. By Vehicle Type
  • 16.2.4. By Propulsion Type
  • 16.2.5. By Vehicle Application
  • 16.2.6. By Country

Chapter 17. South America Autonomous Driving Market Analysis

• 17.1. Key Insights
• 17.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 17.2.1. By Component
  • 17.2.2. By Autonomous Level
  • 17.2.3. By Vehicle Type
  • 17.2.4. By Propulsion Type
  • 17.2.5. By Vehicle Application
  • 17.2.6. By Country

Chapter 18. China Autonomous Driving Market Analysis

• 18.1. Key Insights
• 18.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 18.2.1. By Component
  • 18.2.2. By Autonomous Level
  • 18.2.3. By Vehicle Type
  • 18.2.4. By Propulsion Type
  • 18.2.5. By Vehicle Application

Chapter 19. Japan Autonomous Driving Market Analysis

• 19.1. Key Insights
• 19.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 19.2.1. By Component
  • 19.2.2. By Autonomous Level
  • 19.2.3. By Vehicle Type
  • 19.2.4. By Propulsion Type
  • 19.2.5. By Vehicle Application

Chapter 20. India Autonomous Driving Market Analysis

• 20.1. Key Insights
• 20.2. Market Size and Forecast, 2020-2033 (US$ Bn)
  • 20.2.1. By Component
  • 20.2.2. By Autonomous Level
  • 20.2.3. By Vehicle Type
  • 20.2.4. By Propulsion Type
  • 20.2.5. By Vehicle Application

Chapter 21. Company Profile (Company Overview, Financial Matrix, Key Type landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook)

• 21.1. NVIDIA Corporation
• 21.2. IPG Automotive GmbH
• 21.3. KPIT Technologies Ltd
• 21.4. Waymo LLC
• 21.5. Aptiv PLC
• 21.6. Infineon Technologies AG
• 21.7. Motional, Inc .
• 21.8. Tesla Inc.
• 21.9. Other Prominent Players

Chapter 22. Annexure

• 22.1. List of Secondary Autonomous Levels
• 22.2. Key Country Markets - Marco Economic Outlook/Indicators