한글목차

세계 ADAS 센서 시장은 자동차 안전 기능에 대한 수요 증가, 엄격한 규제, 자율주행 추진으로 인해 빠르게 성장하고 있으며, 첨단운전자보조시스템(ADAS)는 센서, 카메라 등의 기술을 결합하여 차량 주변 정보를 수집하고 운전자에게 도움을 주는 기술입니다. ADAS의 기능은 크루즈 컨트롤과 같은 기본적인 기능부터 차선 유지 지원, 자동 긴급 제동, 어댑티브 크루즈 컨트롤과 같은 첨단 기능까지 다양합니다. 자동차의 자율화가 진전되고 전 세계적으로 안전 규제가 강화되는 가운데 ADAS 센서는 자동차 기술의 미래를 형성하는 데 중요한 역할을 하고 있습니다.

이 보고서는 세계 ADAS 센서 시장에 대해 조사 분석했으며, 시장 규모 예측, 규제 영향, 기술 동향 및 혁신, 경쟁 상황 등의 정보를 제공합니다.

목차

제1장 주요 요약

• 자율주행 기술
  • 자동화 레벨
  • 자율주행 기능
  • 자율주행차 센서
  • 로드맵
• ADAS·자율주행 기술용 센서
  • 센서 요건
  • 센서 스위트 비용
  • 프런트 레이더 센서
  • 사이드 레이더
  • 자동차용 카메라
  • 자동차의 LiDAR
• 대중 시장 차량의 성공적인 ADAS 구현
• ADAS 통합에서 OEM의 과제
• 고급차의 혁신적인 ADAS 솔루션
• 리얼 월드 컨디션의 ADAS 퍼포먼스
• 시장 성장 촉진요인
  • 안전 규제와 NCAP 요건
  • 첨단 안전 기능에 대한 소비자 수요
  • 차량 자율화를 향한 진보
  • 센서 기술 비용 절감
• 시장 성장 억제요인
  • 첨단 ADAS 시스템의 고비용
  • 센서 신뢰성의 기술적 과제
  • 소비자 신뢰와 수용 문제
  • 사이버 보안 우려
• 시장 기회
  • ADAS와 V2X 기술의 통합
  • 애프터마켓 ADAS 솔루션
  • 상용차·플릿의 ADAS
  • ADAS 기술 신흥 시장
• 시장 과제
• 경쟁 상황
  • 주요 기업의 경쟁 포지셔닝
  • ADAS 기술의 투자 동향

제2장 소개

• 자율주행
• 현대 자동차의 ADAS 중요성
• ADAS 공급망의 주요 기업

제3장 시장 개요

• 세계의 ADAS 시장 규모와 성장
  • 유형별
  • 지역별
• ADAS 채용을 촉진하는 규제 상황
• ADAS 시장에 대한 자율주행차 개발의 영향

제4장 ADAS 센서 기술

• 주요 ADAS 센서 유형 개요
• ADAS 컨트롤러와 ECU
• ADAS 컨트롤러의 주요 기술
• 새로운 센서 기술

제5장 주요 시장 기업과 시장 점유율

• 세계의 Tier-1 시장 점유율 분석
• ADAS 센서 시장 전체의 점유율
• 시장 점유율 변동 : 지역별
• 전방 카메라 시장 점유율
• 운전자 모니터링 시스템(DMS)/탑승자 모니터링 시스템(OMS) 시장 점유율
• 기술의 진보가 시장 성장을 촉진
• DMS/OMS 채용에 대한 규제의 영향
• LiDAR 시장 점유율
• 레이더 시장 점유율
• 기타 ADAS 센서
• ADAS 컨트롤러와 ECU 시장 점유율
• 주요 Tier-1 공급업체 분석

제6장 기술 동향과 혁신

• 카메라 기술의 진보
• 레이더 기술의 진화
• LiDAR 혁신
• 센서 융합의 진보
• ADAS 컨트롤러 혁신

제7장 향후 전망과 시장 예측

• 시장 예측(2024-2035년)
  • 시장 규모 예측
  • 성장 예측 : 지역별
  • 예측되는 기술 채용률
• ADAS 시장에 대한 자율주행차 개발의 영향
• 잠재적 파괴적 기술과 그러한 영향

제8장 규제 상황

• 세계의 ADAS 관련 규제
• 향후 규제 동향과 시장에 대한 영향

제9장 기업 개요(기업 98개사 프로파일)

제10장 부록

제11장 참고문헌

영문목차

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