자동차용 레이더 온 칩 솔루션 시장 기회, 성장 촉진요인, 산업 동향 분석 및 예측(2025-2034년)

The Global Automotive Radar-on-Chip Solution Market was valued at USD 3.3 billion in 2024 and is estimated to grow at a CAGR of 14.1% to reach USD 12 billion by 2034.

Growing focus on road safety and the rise of regulatory mandates surrounding advanced driver-assistance systems (ADAS) directly driving demand for radar-on-chip technologies. As vehicles increasingly shift toward automation and intelligent driving features, these compact radar solutions offer enhanced detection capabilities for collision avoidance, adaptive cruise control, and traffic assistance. Consumers are pushing automakers to deliver high-performance safety systems that operate efficiently in real-world driving conditions, making radar-on-chip integration essential. These chip-level radar systems allow software-defined functionality while minimizing size, energy consumption, and system weight, key elements in electric vehicle (EV) adoption. Manufacturers are under pressure to meet safety benchmarks without impacting EV performance or range, encouraging the widespread adoption of RoC solutions. As global initiatives for higher vehicle autonomy progress, the need for high-precision, low-latency radar sensing systems becomes even more critical. These solutions support functionalities such as automated lane management and obstacle detection under poor visibility, helping to set the foundation for fully autonomous driving environments.

In 2024, the hardware segment accounted for a 62.4% share and is forecasted to grow at a CAGR of 14.6% through 2034. Hardware leads this space as it includes the integrated RF front-end, antennas, and digital signal processors essential for radar operation. The trend is toward highly integrated single-chip architectures that merge multiple components into a compact form factor. Automotive radar hardware is also transitioning to multi-band and multi-channel capabilities operating across 24, 77, and 79 GHz, offering improved resolution, expanded range, and robust performance across all driving scenarios.

The 77 GHz segment held a 58% share in 2024 and is projected to grow at a CAGR of 13.9% through 2034. These radar-on-chip solutions are setting the benchmark for long-range applications in ADAS and autonomous vehicle platforms. Known for delivering higher resolution, extended detection range, and minimal interference, 77 GHz radar technology is now the go-to frequency for next-generation automotive radar systems.

US Automotive Radar-on-Chip Solution Market held an 86.6% share, generating USD 601 million in 2024. The country's strong foothold in semiconductor innovation, paired with rapid adoption of ADAS features and autonomous vehicle development, drives this leadership. From compact cars to luxury models, vehicles in the US now come equipped with radar-enabled systems that offer adaptive cruise control, emergency braking, and lane assistance. As demand grows for AI-driven, high-resolution radar modules capable of real-time perception, the push for sophisticated 77 GHz multi-channel radar-on-chip solutions continues to accelerate.

Key companies in the Automotive Radar-on-Chip Solution Market are Robert Bosch, ZF Friedrichshafen, Texas Instruments (TI), Infineon Technologies, Continental, Renesas Electronics, and NXP Semiconductors. Leading players in the Automotive Radar-on-Chip Solution Market are heavily investing in R&D to miniaturize hardware while improving performance through advanced signal processing and AI-driven algorithms. Companies are focusing on developing scalable platforms that support multi-band radar operation and integrate with broader ADAS architectures. Strategic partnerships with OEMs enable co-development of radar modules customized for specific vehicle classes. Many firms are also optimizing chipsets to align with EV architectures by reducing energy consumption without compromising accuracy.

Table of Contents

Chapter 1 Methodology

• 1.1 Market scope and definition
• 1.2 Research design
  • 1.2.1 Research approach
  • 1.2.2 Data collection methods
• 1.3 Data mining sources
  • 1.3.1 Regional/Country
• 1.4 Base estimates and calculations
  • 1.4.1 Base year calculation
  • 1.4.2 Key trends for market estimation
• 1.5 Primary research and validation
  • 1.5.1 Primary sources
• 1.6 Forecast
• 1.7 Research assumptions and limitations

Chapter 2 Executive Summary

• 2.1 Industry 360⁰ synopsis, 2021 - 2034
• 2.2 Key market trends
  • 2.2.1 Regional
  • 2.2.2 Component
  • 2.2.3 Frequency band
  • 2.2.4 Range
  • 2.2.5 Integration level
  • 2.2.6 Application
• 2.3 TAM Analysis, 2025-2034
• 2.4 CXO perspectives: Strategic imperatives
  • 2.4.1 Executive decision points
  • 2.4.2 Critical success factors
• 2.5 Future outlook
• 2.6 Strategic recommendations
  • 2.6.1 Supply chain diversification strategy
  • 2.6.2 Product portfolio enhancement
  • 2.6.3 Partnership and alliance opportunities
  • 2.6.4 Cost management and pricing strategy

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Supplier landscape
  • 3.1.2 Profit margin analysis
  • 3.1.3 Cost structure
  • 3.1.4 Value addition at each stage
  • 3.1.5 Factor affecting the value chain
  • 3.1.6 Disruptions
• 3.2 Value chain analysis
  • 3.2.1 Upstream value chain
  • 3.2.2 Midstream value chain
  • 3.2.3 Downstream value chain
• 3.3 Industry impact forces
  • 3.3.1 Growth drivers
    • 3.3.1.1 Increasing ADAS adoption
    • 3.3.1.2 Growth of autonomous vehicles
    • 3.3.1.3 Compact and integrated design
    • 3.3.1.4 Increasing vehicle electrification
  • 3.3.2 Industry pitfalls and challenges
    • 3.3.2.1 High development and production costs
    • 3.3.2.2 Technical challenges in harsh environments
    • 3.3.2.3 Supply chain disruptions
    • 3.3.2.4 Cybersecurity concerns
  • 3.3.3 Market opportunities
    • 3.3.3.1 Expansion in emerging markets
    • 3.3.3.2 Integration with ai and sensor fusion
    • 3.3.3.3 Government incentives and safety regulations
    • 3.3.3.4 Adoption in commercial and fleet vehicles
• 3.4 Growth potential analysis
• 3.5 Regulatory landscape
  • 3.5.1 UNECE regulation no. 152 - advanced emergency braking systems (AEBS)
  • 3.5.2 Eu general safety regulation (GSR) 2024/2144
  • 3.5.3 US federal motor vehicle safety standards (FMVSS)
  • 3.5.4 China miit intelligent and connected vehicle guidelines 2024
  • 3.5.5 Japan mlit autonomous driving safety framework
• 3.6 Porter's analysis
• 3.7 PESTEL analysis
• 3.8 Future trends
• 3.9 Technology and Innovation landscape
  • 3.9.1 Current technologies
    • 3.9.1.1 77 ghz and 79 ghz mmwave radar technology
    • 3.9.1.2 4d imaging radar
    • 3.9.1.3 CMOS and SiGe-based radar socs
  • 3.9.2 Emerging technologies
    • 3.9.2.1 Digital beamforming radar
    • 3.9.2.2 AI-powered radar signal processing
    • 3.9.2.3 Radar-vision sensor fusion Socs
• 3.10 Price trends
  • 3.10.1 By product
  • 3.10.2 By region
• 3.11 Patent analysis
• 3.12 Cost breakdown analysis
• 3.13 Sustainability and environmental aspects
  • 3.13.1 Sustainable practices
  • 3.13.2 Waste reduction strategies
  • 3.13.3 Energy efficiency in production
  • 3.13.4 Eco-friendly Initiatives
  • 3.13.5 Carbon footprint considerations
• 3.14 Vehicle System Integration & Sensor Fusion
  • 3.14.1 Multi-sensor architecture complexity
  • 3.14.2 Radar-camera fusion challenges
  • 3.14.3 Radar-LiDAR integration strategies
  • 3.14.4 ECU integration & processing requirements
  • 3.14.5 Real-time data fusion algorithms
• 3.15 ADAS Application Performance Optimization
  • 3.15.1 Application-specific radar requirements
  • 3.15.2 Range vs resolution trade-offs
  • 3.15.3 Angular resolution enhancement needs
  • 3.15.4 Velocity measurement accuracy
  • 3.15.5 Multi-target detection capabilities
• 3.16 Radar Chip Design & Manufacturing Challenges
  • 3.16.1 Silicon process technology selection
  • 3.16.2 RF circuit design complexity
  • 3.16.3 Antenna-in-package integration
  • 3.16.4 Thermal management solutions
  • 3.16.5 Power consumption optimization
• 3.17 Automotive Supply Chain & Qualification
  • 3.17.1 Automotive-grade component qualification
  • 3.17.2 AEC-Q100 compliance requirements
  • 3.17.3 Long-term supply assurance
  • 3.17.4 Supply chain risk mitigation
• 3.18 Software-Hardware Co-Design Evolution
  • 3.18.1 Software-defined radar architecture
  • 3.18.2 Configurable signal processing
  • 3.18.3 Over-the-air update capabilities
  • 3.18.4 AI algorithm integration
• 3.19 Automotive Safety Standards Compliance
  • 3.19.1 ISO 26262 functional safety requirements
  • 3.19.2 ASIL rating & risk assessment
  • 3.19.3 Safety case development
  • 3.19.4 Hazard analysis & risk assessment (HARA)
• 3.20 Environmental & Operational Challenges
  • 3.20.1 Weather condition performance
  • 3.20.2 Interference mitigation strategies
  • 3.20.3 Multi-path reflection handling
  • 3.20.4 Urban canyon performance
  • 3.20.5 Temperature variation compensation
• 3.21 Cost Optimization & Value Engineering
  • 3.21.1 Chip architecture cost analysis
  • 3.21.2 Integration level vs cost trade-offs
  • 3.21.3 Volume production economics
  • 3.21.4 Total system cost optimization

Chapter 4 Competitive Landscape, 2024

• 4.1 Introduction
• 4.2 Company market share analysis, 2024
  • 4.2.1 North America
  • 4.2.2 Europe
  • 4.2.3 Asia Pacific
  • 4.2.4 Latin America
  • 4.2.5 Middle East Africa
• 4.3 Competitive analysis of major market players
• 4.4 Competitive positioning matrix
• 4.5 Strategic outlook matrix
• 4.6 Key developments
  • 4.6.1 Mergers & acquisitions
  • 4.6.2 Partnerships & collaborations
  • 4.6.3 New Product Launches
  • 4.6.4 Expansion Plans and funding

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2034 ($Mn)

• 5.1 Key trends
• 5.2 Hardware
  • 5.2.1 RF Front-End & Antennas
  • 5.2.2 Signal Processors
  • 5.2.3 Sensor Packaging & Modules
• 5.3 Software
  • 5.3.1 Signal Processing Software
  • 5.3.2 Sensor Fusion & AI Software
  • 5.3.3 Calibration & Testing Software
• 5.4 Services

Chapter 6 Market Estimates & Forecast, By Frequency band, 2021 - 2034 ($Mn)

• 6.1 Key trends
• 6.2 24 GHz
• 6.3 77 GHz
• 6.4 79 GHz

Chapter 7 Market Estimates & Forecast, By Range, 2021 - 2034 ($Mn)

• 7.1 Key trends
• 7.2 Short-Range Radar (SRR)
• 7.3 Medium-Range Radar (MRR)
• 7.4 Long-Range Radar (LRR)
• 7.5 Imaging Radar

Chapter 8 Market Estimates & Forecast, By Integration level, 2021 - 2034 ($Mn)

• 8.1 Key trends
• 8.2 Transceiver-Only Radar-on-Chip
• 8.3 Complete Radar SoC (System-on-Chip)
• 8.4 Digital/Imaging Radar Chips

Chapter 9 Market Estimates & Forecast, By Application, 2021 - 2034 ($Mn)

• 9.1 Key trends
• 9.2 ADAS Safety Systems
  • 9.2.1 Blind-spot detection (BSD)
  • 9.2.2 Autonomous emergency braking (AEB)
  • 9.2.3 Adaptive cruise control (ACC)
  • 9.2.4 Collision avoidance
• 9.3 Autonomous Driving Functions
  • 9.3.1 Highway autopilot
  • 9.3.2 Urban automated driving
  • 9.3.3 Sensor fusion
• 9.4 In cabin solution
• 9.5 EV specific solutions

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2034 ($Mn)

• 10.1 Key trends
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 France
  • 10.3.4 Italy
  • 10.3.5 Spain
  • 10.3.6 Russia
  • 10.3.7 Nordics
  • 10.3.8 Poland
• 10.4 Asia Pacific
  • 10.4.1 China
  • 10.4.2 India
  • 10.4.3 Japan
  • 10.4.4 South Korea
  • 10.4.5 ANZ
  • 10.4.6 Vietnam
  • 10.4.7 Singapore
  • 10.4.8 Indonesia
• 10.5 Latin America
  • 10.5.1 Brazil
  • 10.5.2 Mexico
  • 10.5.3 Argentina
• 10.6 MEA
  • 10.6.1 South Africa
  • 10.6.2 Saudi Arabia
  • 10.6.3 UAE

Chapter 11 Company Profiles

• 11.1 Global players
  • 11.1.1 Texas Instruments
  • 11.1.2 NXP Semiconductors
  • 11.1.3 Infineon Technologies
  • 11.1.4 Analog Devices
  • 11.1.5 STMicroelectronics
  • 11.1.6 Renesas Electronics
  • 11.1.7 Qualcomm Technologies
  • 11.1.8 Broadcom
• 11.2 Regional players
  • 11.2.1 Continental
  • 11.2.2 Robert Bosch
  • 11.2.3 Denso
  • 11.2.4 Aptiv
  • 11.2.5 Valeo
  • 11.2.6 Magna International
  • 11.2.7 ZF Friedrichshafen
  • 11.2.8 Veoneer (Arriver)
• 11.3 Emerging players and disruptors
  • 11.3.1 Arbe Robotics
  • 11.3.2 Oculii Corp (Ambarella)
  • 11.3.3 Uhnder
  • 11.3.4 Steradian Semiconductors
  • 11.3.5 Echodyne
  • 11.3.6 Metawave
  • 11.3.7 Ainstein AI
  • 11.3.8 RFISee
  • 11.3.9 Vayyar Imaging