세계의 ADAS 센서 시장(2025-2035년)

The ADAS sensors market is experiencing rapid growth driven by increasing demand for vehicle safety features, stringent regulations, and the push towards autonomous driving. Advanced Driver Assistance Systems (ADAS) use a combination of sensors, cameras, and other technologies to gather information about the vehicle's surroundings and provide assistance to the driver. ADAS features can range from basic functionalities like cruise control to more advanced capabilities such as lane keeping assist, automatic emergency braking, and adaptive cruise control. This comprehensive market report provides an in-depth analysis of the Advanced Driver Assistance Systems (ADAS) sensors market, projecting trends and growth from 2025 to 2035. As vehicles become increasingly autonomous and safety regulations tighten globally, ADAS sensors are playing a crucial role in shaping the future of automotive technology.

Report contents include:

• Detailed market size projections for ADAS sensors, broken down by sensor type, units, and regional markets from 2024 to 2035.
• In-depth examination of key ADAS sensor technologies including cameras, radar, LiDAR, ultrasonic sensors, and infrared sensors, as well as emerging technologies like event-based vision and quantum dot optical sensors.
• Competitive Landscape: Analysis of global Tier-1 suppliers, market share data for various sensor types, and profiles of over 95 key players in the ADAS ecosystem. Companies profiled include 7invensu, Acconeer AB, Actronika, Aeva, AEye, AMS Osram, Aptiv, Arbe, Aryballe, AutoX Technologies Inc., Baidu, Baraja, Beijing Surestar Technology, Benewake, Bosch, Cepton Inc., Continental AG, Cruise, DeepWay, Denso Corporation, Echodyne Inc., EM Infinity, Emberion Oy, Emotion3D, Epicnpoc, Eyeris, Greenerwave, Hesai Technology, Huawei, Hyundai Mobis, Inceptio Technology, Innoviz Technologies, Kognic, Koito Manufacturing, LeddarTech, Leishen Intelligent System Co. Ltd., Li Auto, Lidwave, Livox, Lumentum Operations LLC, Luminar Technologies, Lumotive, Lunewave, Magna International, Melexis, Metahelios, Metawave Corporation, Mitsubishi Electric, Mobileye, Nodar, NXP, Ommatidia LiDAR, OmniVision, Onsemi, OQmented, Ouster, Owl Autonomous Imaging, OPmobility, plus.ai, Pontosense, Pony.ai, PreAct, Prophesee, Qualcomm, Quanergy, Recogni, Renesas Electronics Corporation, RoboSense, Seeing Machines, Sensrad, Seyond, SenseTime, SiLC Technologies, Smart Radar System Inc., Spartan Radar, Steerlight, Tactile Mobility, Tanway, Terabee, Texas Instruments, Tobii, Uhnder, Ultraleap, Valeo, Vayyar, Velodyne Lidar, Veoneer, Visteon, Voyant Photonics, Vueron, Waymo, Wayve, XenomatiX, XPeng Motors, Zadar Labs, Zendar, ZF Friedrichshafen AG, Zvision.
• Overview of global ADAS-related regulations and their influence on market growth and technology adoption.
• Insights into potential disruptive technologies, the impact of autonomous vehicle development on the ADAS market, and long-term growth projections.

• Market segmentation analysis by sensor type, including:
  • Cameras: Front-facing, surround-view, driver monitoring, and infrared cameras
  • Radar: Short-range, long-range, and imaging radar systems
  • LiDAR: Mechanical, solid-state, and MEMS-based LiDAR technologies
  • Ultrasonic Sensors: For parking assistance and short-range object detection
  • Infrared Sensors: For enhanced night vision and pedestrian detection

• Market restraints such as high costs of advanced ADAS systems, technical challenges in sensor reliability, and cybersecurity concerns.

• Technology Trends and Innovations including:
  • Cameras: Developments in high-resolution sensors, wide dynamic range capabilities, and AI-enhanced image processing.
  • Radar: Evolution of 4D imaging radar, high-resolution radar, and software-defined radar systems
  • LiDAR: Innovations in solid-state LiDAR, MEMS-based LiDAR, and FMCW LiDAR, along with cost reduction strategies
  • Sensor Fusion: Advancements in multi-sensor data fusion algorithms, edge computing, and AI-driven sensor fusion techniques
  • ADAS Controllers: Trends in high-performance computing platforms, domain controllers, and zonal architecture

• Competitive Landscape analysis including:
  • Global Tier-1 market share analysis
  • Market share data for specific sensor types (e.g., front cameras, LiDAR, radar)
  • Analysis of major Tier-1 suppliers and their strategies
  • Global regulatory environment for ADAS technologies.

Key Questions Addressed:

• 1. What is the projected market size for ADAS sensors by 2035?
• 2. Which sensor technologies are expected to see the highest growth rates?
• 3. How will regulatory requirements drive ADAS sensor adoption in different regions?
• 4. What are the key challenges facing ADAS sensor manufacturers?
• 5. How will the shift towards autonomous vehicles impact the ADAS sensors market?
• 6. Which companies are leading in different sensor categories, and what are their market shares?
• 7. What emerging technologies could disrupt the current ADAS sensor landscape?

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

• 1.1. Autonomous driving technologies
  • 1.1.1. Automation Levels
  • 1.1.2. Functions of autonomous driving
  • 1.1.3. Sensors in autonomous vehicles
  • 1.1.4. Roadmap
• 1.2. Sensors for ADAS and Autonomous Technologies
  • 1.2.1. Sensor Requirements
  • 1.2.2. Sensor Suite Costs
  • 1.2.3. Front radar sensors
  • 1.2.4. Side Radars
  • 1.2.5. Vehicle Cameras
  • 1.2.6. LiDARs in Automotive
• 1.3. Successful ADAS Implementation in Mass-Market Vehicles
• 1.4. Challenges Faced by OEMs in ADAS Integration
• 1.5. Innovative ADAS Solutions in Premium Vehicles
• 1.6. ADAS Performance in Real-World Conditions
• 1.7. Market Drivers
  • 1.7.1. Safety Regulations and NCAP Requirements
  • 1.7.2. Consumer Demand for Advanced Safety Features
  • 1.7.3. Progress Towards Vehicle Autonomy
  • 1.7.4. Cost Reductions in Sensor Technologies
• 1.8. Market Restraints
  • 1.8.1. High Costs of Advanced ADAS Systems
  • 1.8.2. Technical Challenges in Sensor Reliability
  • 1.8.3. Consumer Trust and Acceptance Issues
  • 1.8.4. Cybersecurity Concerns
• 1.9. Market Opportunities
  • 1.9.1. Integration of ADAS with V2X Technologies
  • 1.9.2. Aftermarket ADAS Solutions
  • 1.9.3. ADAS in Commercial Vehicles and Fleets
  • 1.9.4. Emerging Markets for ADAS Technologies
• 1.10. Market Challenges
• 1.11. Competitive landscape
  • 1.11.1. Competitive Positioning of Key Players
  • 1.11.2. Investment Trends in ADAS Technologies

2. INTRODUCTION

• 2.1. Autonomous driving
  • 2.1.1. Overview
  • 2.1.2. Autonomous driving development in the industry
    • 2.1.2.1. Evolutionary Approach
    • 2.1.2.2. Revolutionary Approach
  • 2.1.3. Position navigation technology
  • 2.1.4. Electric Vehicles and Autonomy
  • 2.1.5. Passive and Active Sensors
  • 2.1.6. Sensor fusion
    • 2.1.6.1. Evolution of Sensor Suite
    • 2.1.6.2. Vison-only and Multi-sensor Fusion Approaches
    • 2.1.6.3. Trends
    • 2.1.6.4. Hybrid AI
    • 2.1.6.5. Pure vision vs lidar sensor fusion
  • 2.1.7. Optical 3D sensing
  • 2.1.8. Multi-camera
    • 2.1.8.1. Overview
    • 2.1.8.2. Structured light
    • 2.1.8.3. 3D depth-aware imaging technologies
    • 2.1.8.4. Resolution
  • 2.1.9. Radar and lidar
  • 2.1.10. Emerging Sensor Technologies
    • 2.1.10.1. Event-based Cameras
    • 2.1.10.2. Quantum Sensors
    • 2.1.10.3. Metamaterial-based Sensors
    • 2.1.10.4. Sensor-on-Chip Solutions
• 2.2. Importance of ADAS in Modern Vehicles
• 2.3. Key Players in the ADAS Supply Chain

3. MARKET OVERVIEW

• 3.1. Global ADAS Market Size and Growth
  • 3.1.1. By type
  • 3.1.2. By region
    • 3.1.2.1. Regional ADAS Adoption Trends
• 3.2. Regulatory Landscape Driving ADAS Adoption
• 3.3. Impact of Autonomous Vehicle Development on ADAS Market

4. ADAS SENSOR TECHNOLOGIES

• 4.1. Overview of Key ADAS Sensor Types
  • 4.1.1. Sensors in Autonomous Vehicles
    • 4.1.1.1. Number of sensors
    • 4.1.1.2. Cost
    • 4.1.1.3. V2X, 5G, advanced digital mapping, and GPS in autonomous driving
      • 4.1.1.3.1. V2X Communication
      • 4.1.1.3.2. 5G Networks
      • 4.1.1.3.3. Advanced Digital Mapping
      • 4.1.1.3.4. GPS in Autonomous Driving
  • 4.1.2. Cameras
    • 4.1.2.1. External Cameras
    • 4.1.2.2. E-mirrors
    • 4.1.2.3. Internal Cameras
    • 4.1.2.4. Front camera
    • 4.1.2.5. RGB/Visible light camera
    • 4.1.2.6. CMOS image sensors
      • 4.1.2.6.1. Front vs backside illumination
      • 4.1.2.6.2. Image capture
        • 4.1.2.6.2.1. Rolling Shutter
        • 4.1.2.6.2.2. Global Shutter
      • 4.1.2.6.3. Companies
    • 4.1.2.7. IR Cameras
    • 4.1.2.8. Driver Monitoring Systems (DMS) and Occupant Monitoring Systems (OMS)
      • 4.1.2.8.1. Overview
      • 4.1.2.8.2. 2D Cameras
      • 4.1.2.8.3. 3D Cameras
        • 4.1.2.8.3.1. ToF Cameras
        • 4.1.2.8.3.2. Occupant Monitoring System (OMS) cameras
        • 4.1.2.8.3.3. Flash LiDAR
      • 4.1.2.8.4. NIR/IR Imaging
        • 4.1.2.8.4.1. IR cameras/sensors
        • 4.1.2.8.4.2. Infrared (IR) in DMS
        • 4.1.2.8.4.3. Thermal Cameras in Autonomous Vehicles
        • 4.1.2.8.4.4. Short-Wave Infra-Red (SWIR) Imaging
        • 4.1.2.8.4.5. VCSEL
        • 4.1.2.8.4.6. Market for IR Cameras
        • 4.1.2.8.4.7. Costs
      • 4.1.2.8.5. Eye Movement Tracking
        • 4.1.2.8.5.1. Overview
        • 4.1.2.8.5.2. Event-Based Vision for Eye-Tracking
      • 4.1.2.8.6. Brain Function Monitoring
        • 4.1.2.8.6.1. Overview
        • 4.1.2.8.6.2. Magnetoencephalography
      • 4.1.2.8.7. Cardiovascular Metrics
    • 4.1.2.9. E-mirrors
    • 4.1.2.10. Companies
  • 4.1.3. Radar
    • 4.1.3.1. Radar in Autonomous Vehicles
      • 4.1.3.1.1. Localization
      • 4.1.3.1.2. Radar mapping
      • 4.1.3.1.3. Waveforms
      • 4.1.3.1.4. Frequencies
    • 4.1.3.2. Front Radar
    • 4.1.3.3. Side Radars
    • 4.1.3.4. Components
    • 4.1.3.5. Radar trends
      • 4.1.3.5.1. Imaging
      • 4.1.3.5.2. Resolution
      • 4.1.3.5.3. Automotive radar boards
      • 4.1.3.5.4. Volume and Footprint
      • 4.1.3.5.5. Packaging and Performance
      • 4.1.3.5.6. Increasing Range
      • 4.1.3.5.7. Field of View
      • 4.1.3.5.8. Virtual Channel Count
        • 4.1.3.5.8.1. Digital Beamforming (DBF)
        • 4.1.3.5.8.2. Sparse Array Designs
    • 4.1.3.6. In-Cabin Radars
    • 4.1.3.7. 4D Radars and Imaging Radars
      • 4.1.3.7.1. Overview
      • 4.1.3.7.2. Commerical examples
      • 4.1.3.7.3. Drivers for 4D and imaging radars
      • 4.1.3.7.4. Approaches to Achieve 4D Imaging Radar Capabilities
    • 4.1.3.8. Transceivers
      • 4.1.3.8.1. Commercial examples
      • 4.1.3.8.2. Transceiver technology evolution
        • 4.1.3.8.2.1. CMOS
        • 4.1.3.8.2.2. SiGe BiCMOS
        • 4.1.3.8.2.3. FD-SOI
    • 4.1.3.9. Radomes
      • 4.1.3.9.1. Overview
      • 4.1.3.9.2. Materials
        • 4.1.3.9.2.1. Dielectric Constant
        • 4.1.3.9.2.2. Loss Tangent
      • 4.1.3.9.3. Commercial examples
    • 4.1.3.10. Antennas
      • 4.1.3.10.1. Designs
      • 4.1.3.10.2. Phased Array Antennas
      • 4.1.3.10.3. Metamaterials
      • 4.1.3.10.4. 3D Printed Antennas
    • 4.1.3.11. Semiconductors
    • 4.1.3.12. Companies
    • 4.1.3.13. Markets for Radar
    • 4.1.3.14. Radar versus LiDAR
  • 4.1.4. LiDAR
    • 4.1.4.1. Automotive LiDAR
      • 4.1.4.1.1. Operating process
      • 4.1.4.1.2. Requirements
    • 4.1.4.2. LiDAR systems
      • 4.1.4.2.1. Commercialization
      • 4.1.4.2.2. Automotive LiDAR Supply Chain
      • 4.1.4.2.3. Pricing and costs
    • 4.1.4.3. Lidar integration in ADAS/AV
      • 4.1.4.3.1. Lamps
      • 4.1.4.3.2. Grille
      • 4.1.4.3.3. On/In the Roof
      • 4.1.4.3.4. Other Positions
    • 4.1.4.4. LiDAR Certification
    • 4.1.4.5. 2D vs 3D lidar
    • 4.1.4.6. Ranging and photodetection
      • 4.1.4.6.1. Direct TOF
      • 4.1.4.6.2. Indirect TOF
    • 4.1.4.7. Frequency Modulated Continuous Wave (FMCW) and Pseudo-Random Noise Modulated Continuous Wave (PMCW)
    • 4.1.4.8. Beam steering
      • 4.1.4.8.1. Mechanical Lidar
      • 4.1.4.8.2. MEMS Lidar
        • 4.1.4.8.2.1. Commercial MEMS-based LiDAR systems
      • 4.1.4.8.3. Flash lidar
      • 4.1.4.8.4. Optical phased array (OPA) Lidar
        • 4.1.4.8.4.1. Overview
        • 4.1.4.8.4.2. Approaches
      • 4.1.4.8.5. Other technologies
        • 4.1.4.8.5.1. Spectral deflection
        • 4.1.4.8.5.2. Micro-motion technology
        • 4.1.4.8.5.3. Liquid crystal lidar
        • 4.1.4.8.5.4. Metamaterials
        • 4.1.4.8.5.5. GLV-based beam steering
        • 4.1.4.8.5.6. Liquid lens
        • 4.1.4.8.5.7. Electro-Optical Deflectors
        • 4.1.4.8.5.8. Acousto-optical deflectors
    • 4.1.4.9. Lasers
      • 4.1.4.9.1. IR emitters
      • 4.1.4.9.2. Edge-emitting lasers (EEL)
      • 4.1.4.9.3. Vertical-cavity surface-emitting lasers (VCSEL)
      • 4.1.4.9.4. External cavity & quantum cascade lasers (QCL)
      • 4.1.4.9.5. Fiber lasers
        • 4.1.4.9.5.1. Laser Source Wavelengths
        • 4.1.4.9.5.2. Fiber Amplifiers
      • 4.1.4.9.6. Diode-pumped solid-state lasers (DPSSL)
    • 4.1.4.10. Receivers
    • 4.1.4.11. Signal and data analysis/processing
      • 4.1.4.11.1. Point cloud
        • 4.1.4.11.1.1. 3D Point Cloud Modeling
        • 4.1.4.11.1.2. Reflection Complication
        • 4.1.4.11.1.3. Background Noise & Interference
        • 4.1.4.11.1.4. TOF LiDAR's Spatial Data Analysis
        • 4.1.4.11.1.5. FMCW LiDAR data processing
    • 4.1.4.12. Lidar cleaning
      • 4.1.4.12.1. Overview
      • 4.1.4.12.2. Types
    • 4.1.4.13. LiDAR challenges
    • 4.1.4.14. Companies
• 4.2. ADAS Controllers and ECUs
  • 4.2.1. Role of ADAS Controllers and ECUs in Autonomous Driving
  • 4.2.2. ADAS Controllers: Functions and Technologies
    • 4.2.2.1. Core Functions of ADAS Controllers
    • 4.2.2.2. Key Technologies in ADAS Controllers
• 4.3. Key Technologies in ADAS Controllers
  • 4.3.1.1. ADAS Controller Architectures
  • 4.3.1.2. Types of ECUs in Autonomous Vehicles
    • 4.3.1.2.1. ECU Integration and Communication
  • 4.3.2. Thermal Management
    • 4.3.2.1. Thermal Management Strategies
    • 4.3.2.2. Emerging Technologies in Thermal Management
    • 4.3.2.3. Thermal Interface Materials in ECUs
    • 4.3.2.4. Commercial solutions
  • 4.3.3. Challenges in ADAS Controllers and ECUs for Autonomous Driving
  • 4.3.4. Future Trends and Developments
    • 4.3.4.1. Advanced AI and Machine Learning
    • 4.3.4.2. Edge Computing and Distributed Intelligence
    • 4.3.4.3. Software-Defined Vehicles
    • 4.3.4.4. Integration of V2X Communication
    • 4.3.4.5. Future Trends
• 4.4. Emerging Sensor Technologies
  • 4.4.1. Event-based Vision
    • 4.4.1.1. Data
    • 4.4.1.2. Event-based Sensing
  • 4.4.2. Quantum Dot Optical Sensors
    • 4.4.2.1. Properties
    • 4.4.2.2. Infrared (IR) and near-infrared (NIR) sensing
    • 4.4.2.3. Commercial examples
  • 4.4.3. Hyperspectral Imaging

5. KEY MARKET PLAYERS AND MARKET SHARE

• 5.1. Global Tier-1 Market Share Analysis
• 5.2. Overall ADAS Sensor Market Share
• 5.3. Regional Market Share Variations
• 5.4. Front Camera Market Share
  • 5.4.1. Leading Suppliers and Their Market Positions
  • 5.4.2. Technology Differentiators Among Top Players
  • 5.4.3. OEM Partnerships and Supply Agreements
• 5.5. Driver Monitoring Systems (DMS) / Occupant Monitoring Systems (OMS) Market Share
  • 5.5.1. Key Players in the DMS/OMS Space
• 5.6. Technological Advancements Driving Market Growth
• 5.7. Regulatory Impacts on DMS/OMS Adoption
• 5.8. LiDAR Market Share
  • 5.8.1. Current Market Leaders in Automotive LiDAR
  • 5.8.2. Emerging Players and Disruptive Technologies
  • 5.8.3. LiDAR Adoption Trends Among OEMs
• 5.9. Radar Market Share
  • 5.9.1. Market Players in Automotive Radar
    • 5.9.1.1. All Radar
    • 5.9.1.2. Front Radar
    • 5.9.1.3. Side Radar
    • 5.9.1.4. Regional trends
    • 5.9.1.5. Commercial radar models
    • 5.9.1.6. Future Trends
    • 5.9.1.7. Challenges
  • 5.9.2. Imaging Radar vs. Traditional Radar Market Dynamics
    • 5.9.2.1. Trends
    • 5.9.2.2. Packaging and Integration Trends
  • 5.9.3. Frequency Trends (24GHz, 77GHz, 79GHz)
• 5.10. Other ADAS Sensors
  • 5.10.1. Ultrasonic Sensors
  • 5.10.2. Infrared Sensors
  • 5.10.3. GNSS and IMU Suppliers
• 5.11. ADAS Controllers and ECUs Market Share
  • 5.11.1. Leading Suppliers of ADAS Computing Platforms
  • 5.11.2. Trends in Centralized vs. Distributed ADAS Architectures
• 5.12. Analysis of Major Tier-1 Suppliers

6. TECHNOLOGY TRENDS AND INNOVATIONS

• 6.1. Advancements in Camera Technology
  • 6.1.1. High-Resolution Sensors
  • 6.1.2. Wide Dynamic Range (WDR) Capabilities
  • 6.1.3. Low-Light Performance Improvements
  • 6.1.4. AI-Enhanced Image Processing
• 6.2. Radar Technology Evolution
  • 6.2.1. 4D Imaging Radar
  • 6.2.2. High-Resolution Radar
  • 6.2.3. Software-Defined Radar
• 6.3. LiDAR Innovations
  • 6.3.1. Solid-State LiDAR
  • 6.3.2. MEMS-based LiDAR
  • 6.3.3. FMCW LiDAR
  • 6.3.4. Cost Reduction Strategies
• 6.4. Sensor Fusion Advancements
  • 6.4.1. Multi-Sensor Data Fusion Algorithms
  • 6.4.2. Edge Computing for Sensor Fusion
  • 6.4.3. AI and Machine Learning in Sensor Fusion
• 6.5. ADAS Controller Innovations
  • 6.5.1. High-Performance Computing Platforms
  • 6.5.2. Domain Controllers
  • 6.5.3. Zonal Architecture Trends

7. FUTURE OUTLOOK AND MARKET FORECASTS

• 7.1. Market Forecast (2024-2035)
  • 7.1.1. Market Size Projections
    • 7.1.1.1. By Sensor Type
    • 7.1.1.2. Robotaxis
    • 7.1.1.3. By Units
      • 7.1.1.3.1. Cameras
      • 7.1.1.3.2. Radar
      • 7.1.1.3.3. LiDAR
  • 7.1.2. Regional Growth Forecasts
  • 7.1.3. Expected Technology Adoption Rates
• 7.2. Impact of Autonomous Vehicle Development on ADAS Market
• 7.3. Potential Disruptive Technologies and Their Impact

8. REGULATORY LANDSCAPE

• 8.1. Global ADAS-Related Regulations
  • 8.1.1. Legislation for autonomous vehicles
    • 8.1.1.1. Europe
    • 8.1.1.2. US
    • 8.1.1.3. China
    • 8.1.1.4. Japan
  • 8.1.2. Driver Monitoring Systems (DMS)
• 8.2. Future Regulatory Trends and Their Impact on the Market

9. COMPANY PROFILES (98 company profiles)

10. APPENDICES

• 10.1. Research Methodology
• 10.2. List of Abbreviations

11. REFERENCES