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ADAS感測器的全球市場(2025年～2035年)
市場調查報告書
商品編碼
1537094

ADAS感測器的全球市場(2025年～2035年)

Global ADAS Sensors Market 2025-2035
出版日期: | 出版商: Future Markets, Inc. | 英文 375 Pages, 164 Tables, 48 Figures | 訂單完成後即時交付

價格

由於對車輛安全功能的需求不斷增加、嚴格的法規以及自動駕駛的推動，全球 ADAS 感測器市場正在呈現快速成長。 ADAS（高級駕駛員輔助系統）結合使用感測器、攝影機和其他技術來收集有關車輛周圍環境的資訊並為駕駛員提供協助。 ADAS 功能涵蓋從巡航控制等基本功能到車道維持輔助、自動緊急煞車和自適應巡航控制等高級功能。隨著汽車的自動化程度越來越高以及全球安全法規的收緊，ADAS 感測器在塑造汽車技術的未來方面發揮關鍵作用。

本報告提供全球ADAS感測器市場相關調查分析，提供市場規模的預測，法規的影響，技術趨勢與革新，競爭情形等資訊。

目錄

第1章 摘要整理

  • 自動駕駛技術
    • 自動化層級
    • 自動駕駛的功能
    • 自動駕駛車的感測器
    • 藍圖
  • ADAS·自動駕駛技術感測器
    • 感測器必要條件
    • 感測器套件的成本
    • 服務台雷達感測器
    • 側面雷達
    • 汽車用相機
    • 汽車的LiDAR
  • 數量販車的ADAS的引進成功
  • ADAS整合的OEM的課題
  • 高級車的創新的ADAS解決方案
  • 現實世界條件的ADAS的效能
  • 推動市場要素
    • 安全法規和NCAP必要條件
    • 先進的安全功能的消費者需求
    • 轉動了車輛的自規則化的進步
    • 感應技術的降低成本
  • 阻礙市場要素
    • 先進的ADAS系統的高成本
    • 感測器的可靠性的技術課題
    • 消費者的信賴和接受的問題
    • 網路安全的疑慮
  • 市場機會
    • ADAS和V2X技術的整合
    • 售後市場ADAS解決方案
    • 商用車·車隊的ADAS
    • ADAS技術的新興市場
  • 市場課題
  • 競爭情形
    • 主要企業的競爭定位
    • ADAS技術的投資趨勢

第2章 簡介

  • 自動駕駛
  • 現代的汽車的ADAS的重要性
  • ADAS供應鏈的主要企業

第3章 市場概要

  • 全球ADAS的市場規模與成長
    • 各類型
    • 各地區
  • 促進ADAS的招聘的法規形勢
  • 自動駕駛車開發對ADAS市場的影響

第4章 ADAS感應技術

  • 主要的ADAS感測器的類型概要
  • ADAS控制器和ECU
  • ADAS控制器的主要技術
  • 新的感應技術

第5章 主要市場企業與市場佔有率

  • 全球Tier-1的市場佔有率分析
  • ADAS感測器市場整體佔有率
  • 市場佔有率的變動:各地區
  • 前置相機的市場佔有率
  • 駕駛監控系統(DMS)/乘務員監控系統(OMS)的市場佔有率
  • 技術的進步促進市場成長
  • DMS/OMS招聘的法規影響
  • LiDAR的市場佔有率
  • 雷達的市場佔有率
  • 其他的ADAS感測器
  • ADAS控制器和ECU的市場佔有率
  • 主要Tier-1供應商的分析

第6章 技術趨勢與革新

  • 相機技術的進步
  • 雷達技術的演進
  • LiDAR革新
  • 感測融合的進步
  • ADAS控制器的革新

第7章 未來發展預測與市場預測

  • 市場預測(2024年～2035年)
    • 市場規模的預測
    • 成長預測:各地區
    • 預測的技術招聘率
  • 對ADAS市場的自動駕駛車開發的影響
  • 潛在的顛覆性技術和那些的影響

第8章 法規形勢

  • 全球ADAS相關法規
  • 未來的法規趨勢與市場的影響

第9章 企業簡介(企業98公司的簡介)

第10章 附錄

第11章 參考文獻

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

List of Tables

  • Table 1. Automation Levels
  • Table 2. Functions of Autonomous Driving at Different Levels
  • Table 3. "Big Three" sensors used in Advanced Driver Assistance Systems (ADAS)
  • Table 4. Sensor Requirements for Different Levels of Autonomy
  • Table 5. Sensor Suite for Autonomous Cars-Costs
  • Table 6. Estimated Sensor Suite Costs for Different Levels of Autonomy
  • Table 7. Front Radar Applications in ADAS
  • Table 8. Vehicle Camera Applications in ADAS
  • Table 9. LiDAR Types and Characteristics
  • Table 10. LiDAR Applications in Automotive Systems
  • Table 11. Examples of advanced safety features in mainstream models
  • Table 12. Challenges Faced by OEMs in ADAS Integration
  • Table 13. Innovative ADAS Solutions in Premium Vehicles
  • Table 14. ADAS Performance in Real-World Conditions
  • Table 15. Market drivers for ADAS sensors
  • Table 16. Safety Regulations and NCAP Requirements
  • Table 17. Cost reductions in key sensor technologies
  • Table 18. Market Restraints for ADAS sensors
  • Table 19. Costs of Advanced ADAS Systems
  • Table 20. Technical Challenges in Sensor Reliability
  • Table 21. Market opportunities in ADAS sensors
  • Table 22. ADAS in Commercial Vehicles and Fleets
  • Table 23. Emerging Markets for ADAS Technologies
  • Table 24. Market challenges in ADAS sensors
  • Table 25. Emerging Players and Startups in the ADAS Ecosystem
  • Table 26. Key autonomous driving technologies
  • Table 27. Position navigation technologies
  • Table 28. Autonomous driving sensor comparison
  • Table 29. Recommended Sensor Suites For SAE Level 2 to Level 4 & Robotaxi
  • Table 30. Sensor Fusion Technology Trends for Applications
  • Table 31. Pure vision vs lidar sensor fusion
  • Table 32. Pure vision solution challenges
  • Table 33. Optical 3D sensing methods
  • Table 34. Automotive camera hardware
  • Table 35. 3D depth-aware imaging technologies
  • Table 36. General resolution requirements for different sensors and applications
  • Table 37. ADAS/AV sensor operating wavelength
  • Table 38. Radar hardware
  • Table 39. ADAS/AV hardware challenges
  • Table 40. Key Players in the ADAS Supply Chain
  • Table 41. Global market for ADAS sensors 2022-2035 (by type), billions USD
  • Table 42. Global market for ADAS sensors 2022-2035 (by type), billions USD
  • Table 43. Regional ADAS Adoption Trends
  • Table 44. Regulatory Landscape Driving ADAS Adoption
  • Table 45. No. of Sensors Required for Autonomous Cars - Level 0 to Level 4 and Robotaxis/
  • Table 46. Estimated Cost Range of Sensors for Autonomous Vehicles (in USD)
  • Table 47.Vehicle camera applications in a table:
  • Table 48. ADAS Camera Sensors vs Radar Sensors vs Lidar Sensors
  • Table 49. CMOS image sensors vs CCD cameras
  • Table 50. Advantages and disadvantages of IR Cameras
  • Table 51. Applications of DMS
  • Table 52. Sensing Technologies by Features
  • Table 53. Technology Comparison of Radar, ToF and IR Cameras
  • Table 54. Comparison of In-Cabin Sensing Technologies
  • Table 55. 3D Imaging Systems
  • Table 56. 3D imaging systems
  • Table 57. IR VS. VCSEL Light Sources
  • Table 58. Comparative analysis of LEDS and VCSEL
  • Table 59. Applications of IR Imaging
  • Table 60.Companies in VCSEL
  • Table 61. Average IR Camera Per Passenger Car: 2020-2035
  • Table 62. Global Market for IR Cameras for Passenger Cars 2020-2035 (Million Units)
  • Table 63. Global Market for IR Cameras, 2020-2035 (US$ Millions)
  • Table 64. Cost per IR Camera for DMS, 2020-2035 (US$)
  • Table 65. Eye-Tracking Sensor Categories
  • Table 66. Eye-tracking companies
  • Table 67. Event-Based Vision: Pros and Cons
  • Table 68. Market players in cameras and thermal cameras
  • Table 69. Main Methods of Localization
  • Table 70. Front Radar ADAS Applications
  • Table 71. Side Radar ADAS Applications
  • Table 72. Key Radar Components
  • Table 73. Comparison of In-Cabin Radars
  • Table 74. Comparing 4D imaging radar systems
  • Table 75. Vehicles Using 4D Imaging Radars
  • Table 76. Transceiver suppliers
  • Table 77. Typical supply chain for automotive radar transceivers
  • Table 78. Additional participants in the supply chain
  • Table 79. Key Radome Material Suppliers
  • Table 80. Phased array antenna
  • Table 81. Market players in automotive radar
  • Table 82. Global Volume Sales of Radar: 2020-2035 (in millions)
  • Table 83. Radar Per Vehicle 2020-2035
  • Table 84. Cost per In-Cabin Radar (in USD) 2020-2035
  • Table 85. Market Size for In-Cabin Radar: 2020-2035 (in billion USD)
  • Table 86. Number of Radars Shipped per Vehicle, 2020-2035
  • Table 87. Number of Radars Used in SAE Levels 0, 1 & 2
  • Table 88. Global Radar Unit Sales for Different SAE Levels 2020-2035 (Million Units)
  • Table 89.Global Revenues From Radar by SAE Level 2020-2035 (Billion USD)
  • Table 90. Radar versus LiDAR
  • Table 91. LiDAR classifications
  • Table 92.Comparison of lidar product parameters
  • Table 93. Automotive lidar players by technology
  • Table 94. Cost Reduction Approaches for LiDAR systems
  • Table 95. BOM cost for LiDAR
  • Table 96. Typical price composition for LiDAR system
  • Table 97. Forecast for LiDAR Unit Price by Technology to 2030
  • Table 98. 2D versus 3D LiDAR
  • Table 99. Time of Flight (TOF) vs. Frequency Modulated Continuous Wave (FMCW)
  • Table 100. Direct ToF and Indirect ToF
  • Table 101. Comparison of TOF and FMCW LiDAR technologies
  • Table 102. LiDAR beam steering technologies
  • Table 103. Classifications of MEMS Scanner
  • Table 104. Comparative analysis of different MEMS actuation methods:
  • Table 105. Optical phased array (OPA) Lidar
  • Table 106. Technology options for laser illumination
  • Table 107. Comparing laser choices based on key parameters
  • Table 108. IR emitter technologies
  • Table 109. EEL vs VCSEL Comparison
  • Table 110. Wavelength Comparison: 905 nm vs 1550 nm
  • Table 111. Comparison of Common Laser Type & Wavelength Options
  • Table 112. Photodetector Choice for LiDAR
  • Table 113. LiDAR Detector Comparison
  • Table 114. Comparison of Common Photodetectors
  • Table 115. LiDAR Detector Companies
  • Table 116. LiDAR Signal Applications
  • Table 117. TOF LiDAR's Spatial Data Analysis
  • Table 118. LiDAR challenges
  • Table 119. Automotive LiDAR players
  • Table 120. Core Functions of ADAS Controllers
  • Table 121. ADAS Controller Architectures
  • Table 122. Types of ECUs in Autonomous Vehicles
  • Table 123. Thermal Conductivity of TIMs in ECUs/Computers
  • Table 124. Typical operating temperature ranges for different types of TIMs used in ECUs
  • Table 125. Typical density and thermal conductivity ranges for various TIMs used in ECUs
  • Table 126. TIM market for ECUs/ADAS computers 2020-2035 (Millions USD)
  • Table 127. Challenges in ADAS Controllers and ECUs for Autonomous Driving
  • Table 128. Event-based sensing: Pros and cons
  • Table 129. Top 10 Tier-1 Suppliers by Revenue 2023
  • Table 130. Leading Suppliers and Their Market Positions
  • Table 131. Technology Differentiators Among Top Players
  • Table 132. OEM Partnerships and Supply Agreements
  • Table 133. Key Players in the DMS/OMS Space
  • Table 134. Technological Advancements Driving Market Growth
  • Table 135. Current Market Leaders in Automotive LiDAR
  • Table 136. Emerging Players and Disruptive Technologies
  • Table 137. LiDAR Adoption Trends Among OEMs
  • Table 138.Tier One Market Share by Volume (All Radar)
  • Table 139. Tier One Market Share by Revenue (All Radar)
  • Table 140. Tier One Market Share by Revenue (Front Radar)
  • Table 141.Top OEM Front Radar Choices
  • Table 142. Tier One Market Share by Revenue - Side Radar
  • Table 143. Top OEM Side Radar Choices
  • Table 144. Emerging Radar Players
  • Table 145. Imaging Radar vs. Traditional Radar Market Dynamics
  • Table 146. Main Players in Ultrasonic Sensors
  • Table 147. Main Players in Infrared Sensors
  • Table 148. Main Players in GNSS Receivers and IMUs
  • Table 149. Leading Suppliers of ADAS Computing Platforms
  • Table 150. Trends in Centralized vs. Distributed ADAS Architectures
  • Table 151. Key LiDAR cost reduction strategies
  • Table 152. Global market size for autonomous vehicles by SAE level from 2022-2035 (Millions)
  • Table 153. Global Market Size Projections by Sensor Type, Millions USD, 2024-2035
  • Table 154. Global Market Size Projections by Sensor Type, Million Units, 2024-2035,
  • Table 155.Robotaxi Service Revenue 2024-2035 (in million USD)
  • Table 156. Market Size Projections: Cameras, Million Units, 2024-2035
  • Table 157. Market Size Projections: Radar, Million Units, 2024-2035
  • Table 158. Radar Unit Sales by SAE Levels 2022-2035 (in millions)
  • Table 159. Global Market Size Projections: LiDAR, Million Units, 2024-2035
  • Table 160. Global Market Size Projections by Region, Millions USD, 2024-2035
  • Table 161. Expected Technology Adoption Rates for ADAS
  • Table 162. Global ADAS-Related Regulations
  • Table 163. Regional Variations in ADAS Requirements
  • Table 164. Common abbreviations used in the ADAS (Advanced Driver Assistance Systems) sensors market

List of Figures

  • Figure 1. Autonomous vehicles
  • Figure 2. Roadmap of Autonomous Driving Functions in Private Cars
  • Figure 3. Evolution of Sensor Suites
  • Figure 4. Automotive 3D sensing
  • Figure 5. Evolution of ADAS availability
  • Figure 6.. Autonomous Driving Integration with V2X
  • Figure 7.Types of ADAS sensors
  • Figure 8. Perception and sensing for autonomous vehicles under adverse weather conditions
  • Figure 9. Global market for ADAS sensors 2022-2035 (by type), billions USD
  • Figure 10. Global market for ADAS sensors 2022-2035 (by type), billions USD
  • Figure 11. Toyota external camera
  • Figure 12. Side E-Mirror
  • Figure 13. Internal ADAS camera
  • Figure 14. RGB Cameras for Autonomous Vehicles
  • Figure 15. Front vs backside illumination
  • Figure 16. OmniVision Global Shutter Sensor chip
  • Figure 17. ADAS thermal camera images
  • Figure 18. Driver Monitoring System
  • Figure 19. Driver Monitoring Systems (DMS) with S32V234 Vision Processor
  • Figure 20. Infineon DMS - REAL3(TM) ToF Imager IRS2877A(S)
  • Figure 21. Exploded view of Magna's driver monitoring system built into a rearview mirror
  • Figure 22. LG Innotek ToF Camera for DMS
  • Figure 23. PreAct Mojave Flash LiDAR for OMS
  • Figure 24. ADAS/AV Thermal Camera
  • Figure 25. TriEye
  • Figure 26. LANXESS Concept Radar
  • Figure 27. OPMobility Functionalized Bumper
  • Figure 28. Echodyne metamaterial radar mounted on automobile
  • Figure 29. Lunewave 3D printed radar
  • Figure 30. LiDAR working principle
  • Figure 31. Automotive lidar supply chain
  • Figure 32. Metamaterials in automotive applications
  • Figure 33. Lumotive advanced beam steering concept
  • Figure 34. Illustration of EchoDrive operation
  • Figure 35. Emberion Sensor
  • Figure 36. Global market size for autonomous vehicles by SAE level from 2022-2035 (Millions)
  • Figure 37. Market Size Projections: Cameras, Million Units, 2024-2035
  • Figure 38. Market Size Projections: Radar, Million Units, 2024-2035
  • Figure 39. Radar Unit Sales by SAE Levels 2022-2035 (in millions)
  • Figure 40. Global Market Size Projections: LiDAR, Million Units, 2024-2035
  • Figure 41. Market Size Projections by Region, Millions USD, 2024-2035
  • Figure 42. Continental ARS540
  • Figure 43. Schematic of MESA System
  • Figure 44. EchoGuard Radar System
  • Figure 45. (Hesai AT512 LiDAR)
  • Figure 46. Koito Manufacturing LiDAR
  • Figure 47. LIDAR system for autonomous vehicles
  • Figure 48. Light-control metasurface beam-steering chips