センサーフュージョン市場シェア分析、業界動向と統計、成長予測 2026-2031年

センサーフュージョン市場は2025年に87億4,000万米ドル、2026年中に100億4,000万米ドル規模となり、予測期間（2026年～2031年）に年平均成長率（CAGR）12.65%で成長し、2031年には182億1,000万米ドルに達するとMordor Intelligenceでは予測しています。

Mordor Intelligence（モードーインテリジェンス）「センサーフュージョン市場シェア分析、業界動向と統計、成長予測 2026-2031年 – Sensor Fusion Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026 – 2031)」はセンサーフュージョン（センサ融合）の世界市場を調査し、主要セグメント別に分析・予測を行っています。

調査対象セグメント

• オファリング
  • ハードウェア
  • ソフトウェア
• フュージョン方法
  • レーダー+ カメラフュージョン
  • LiDAR + カメラフュージョン
  • レーダー+ LiDAR フュージョン
  • IMU + GPS フュージョン
  • センサーフュージョン (カメラ+ レーダー+ LiDAR)
• アルゴリズムの種類
  • カルマンフィルター（拡張カルマンフィルター、拡張カルマンフィルター）
  • ベイズネットワーク
  • ニューラルネットワーク、深層学習（ディープラーニング／DL）
  • GNSS、INS統合
• 用途
  • 先進運転支援システム（ADAS）
    • ACC
    • AEB
    • ESC
    • FCW
    • 車線維持支援 (LKA)
  • 自動運転（レベル3-5）
  • 家電（AR、VR、スマートフォン、ウェアラブル）
  • ロボティクス＆ドローン
  • 産業自動化＆スマートマニュファクチャリング
  • 防衛＆航空宇宙
• 車両の種類
  • 乗用車
  • 軽量商用車
  • 大型商用車
  • その他の自動運転車
• 地域
  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • スペイン
    • その他の欧州
  • アジア太平洋地域
    • 中国
    • 日本
    • 韓国
    • インド
    • その他のアジア太平洋地域
  • 南米
    • ブラジル
    • アルゼンチン
    • その他の南米
  • 中東
    • サウジアラビア
    • アラブ首長国連邦（UAE）
    • トルコ
    • その他の中東
  • アフリカ
    • 南アフリカ
    • ナイジェリア
    • エジプト
    • その他のアフリカ

ソリッドステートLiDARの継続的なコスト削減、Euro NCAPの安全基準の強化、エッジAIシリコンの技術革新により、自動車メーカーはカメラ、レーダー、LiDAR、慣性計測ユニットを単一のスタックに統合したマルチセンサーシステムへの投資を増やしています。自動車メーカーは再設計コストを削減するため、プラットフォーム全体でセンサーフュージョンハードウェアの標準化を進めており、家電メーカーはクラウドレイテンシの削減とプライバシー規制への準拠のために、デバイス上での推論処理を採用しています。ティア1サプライヤーと半導体大手間の競争激化はハードウェアのマージンを圧迫していますが、サブスクリプションベースのフュージョンソフトウェアと無線による機能追加の増加によって、この傾向は相殺されています。画像レーダーやソフトウェア定義型LiDARのスタートアップ企業への資金流入は、イノベーションサイクルを加速させ、新しい技術の市場投入までの時間を短縮し、冗長性戦略を強化しています。

レポートの主要ポイント

• 提供形態別に見ると、2025年のセンサーフュージョン市場規模においてハードウェアが61.73%のシェアを占め、市場を牽引しました。一方、ソフトウェアは2031年まで年平均成長率（CAGR）12.68%で拡大すると予測されています。
• 融合方式別に見ると、レーダー・カメラソリューションが2025年のセンサーフュージョン市場シェアの43.56%を占め、LiDAR・カメラ組み合わせは2031年まで年平均成長率12.72%で成長すると予測されています。
• アルゴリズムの種類別に見ると、カルマンフィルターが2025年に37.92%のシェアを占めましたが、ニューラルネットワークモデルは2026年から2031年にかけて年平均成長率12.66%で成長すると予測されています。
• アプリケーション別に見ると、ADAS（先進運転支援システム）が2025年の収益の49.83%を占めましたが、レベル3～5の自動運転プラットフォームは年平均成長率12.78%で成長すると予測されています。
• 車種別に見ると、乗用車は2025年の売上高の62.48%を占め、その他の自動運転車は年平均成長率（CAGR）12.73%を記録すると予測されています。
• 地域別に見ると、アジア太平洋地域が2025年の売上高の40.81%を占めて首位となり、中東地域は2031年まで年平均成長率12.75%と最も高い成長率を示すと見込まれています。

Sensor Fusion Market Analysis by Mordor Intelligence

The sensor fusion market size was valued at USD 8.74 billion in 2025 and estimated to grow from USD 10.04 billion in 2026 to reach USD 18.21 billion by 2031, at a CAGR of 12.65% during the forecast period (2026-2031). Sustained cost reductions in solid-state LiDAR, rising Euro NCAP safety mandates, and breakthroughs in edge-AI silicon are shifting original-equipment budgets toward integrated multi-sensor suites that combine cameras, radar, LiDAR, and inertial units in a single stack. Vehicle manufacturers are standardizing sensor-fusion hardware across entire platforms to avoid redesign costs, while consumer-electronics brands adopt on-device inference to cut cloud latency and comply with privacy regulations. Intensifying competition among tier-one suppliers and semiconductor leaders is compressing hardware margins, a trend offset by growth in subscription-based fusion software and over-the-air feature unlocks. Capital inflows into imaging-radar and software-defined LiDAR start-ups are accelerating innovation cycles, reducing time-to-market for new modalities and enhancing redundancy strategies.

Key Report Takeaways

• By offering, hardware led with 61.73% share of the sensor fusion market size in 2025, whereas software is expanding at a 12.68% CAGR through 2031.
• By fusion method, radar-camera solutions commanded 43.56% of sensor fusion market share in 2025, while LiDAR-camera combinations are projected to grow at a 12.72% CAGR to 2031.
• By algorithm type, Kalman filters held 37.92% share in 2025, yet neural-network models are advancing at a 12.66% CAGR during 2026-2031.
• By application, ADAS generated 49.83% of 2025 revenue, but Level 3-5 autonomous platforms are forecast to rise at a 12.78% CAGR.
• By vehicle type, passenger cars accounted for 62.48% of 2025 revenue, while other autonomous vehicles are expected to record a 12.73% CAGR.
• By geography, Asia Pacific led with 40.81% revenue share in 2025, whereas the Middle East is poised for the fastest 12.75% CAGR through 2031.

Note: Market size and forecast figures in this report are generated using Mordor Intelligence’s proprietary estimation framework, updated with the latest available data and insights as of January 2026.

Global Sensor Fusion Market Trends and Insights

Mandate Of Sensor Fusion For Euro NCAP 5-Star Ratings Accelerating European OEM Adoption

Euro NCAP’s 2026 protocols require radar-camera or LiDAR-camera integration to secure a 5-star score, driving immediate redesigns of volume models by European brands. Volkswagen confirmed that all post-2026 MEB launches will carry radar-camera fusion, eliminating single-sensor architectures. Tier-one suppliers with certified middleware are capturing design wins as automakers seek turnkey compliance. The regulation’s global ripple effect is evident in exports to Asia Pacific and South America, where reuse of Euro-spec platforms minimizes engineering divergence. This policy shift entrenches multi-sensor redundancy as a baseline rather than a premium option.

Solid-State LiDAR Cost Decline Enabling Multi-Sensor Suites in Mid-Segment Cars

Hesai is committed to sub-USD 500 solid-state LiDAR by late 2026, leveraging silicon-photonics and volume scaling. BYD already deploys LiDAR-camera-radar arrays in sedans below USD 25,000, widening adoption beyond luxury tiers. Geely’s Galaxy program mirrors this strategy, prompting European and North American peers to accelerate solid-state roadmaps. China’s domestic output is projected to top 2 million LiDAR units annually by 2027, establishing supply-chain leverage that reinforces the region’s leadership in affordable ADAS penetration.

Edge-AI Chip Advancements Allowing Real-Time Multi-Modal Fusion in Mobile and XR Devices

Qualcomm’s Snapdragon 8 Gen 3 integrates a 15-TOPS neural engine that executes multimodal fusion on-device, reducing latency by up to 90% compared to cloud pipelines. Apple’s Vision Pro and Meta’s Quest 3 deliver sub-20 ms motion-to-photon delays through similar approaches, enabling immersive spatial computing without external beacons. On-device inference also supports compliance with GDPR and China’s privacy laws, as raw sensor data remains local. These advancements open the sensor fusion market to smartphones, headsets, and wearables that previously relied on single-sensor solutions.

Deployment of AMR Robots in Smart Factories Demanding High-Accuracy Sensor Fusion

Autonomous mobile robots in automotive and electronics plants depend on centimeter-level localization that fuses LiDAR, stereo vision, IMUs, and ultra-wideband signals. ABB reported 99.7% uptime across its 2025 AMR fleet by isolating faulty sensor inputs through Kalman-based fusion.[1] The International Federation of Robotics forecasts more than 1.2 million AMRs by 2027, with Europe and Asia Pacific leading installations. Multi-sensor fusion also underpins human-robot collaboration, allowing AMRs to predict worker trajectories and adjust routes dynamically.

Lack of Uniform Fusion Architecture Standards Hindering Interoperability

SAE guidelines for ADAS sensor interfaces remain voluntary, while AUTOSAR, ROS 2, and proprietary stacks compete for dominance.[2] Automakers incur higher engineering costs when swapping sensor suppliers, and over-the-air updates demand time-consuming revalidation across divergent protocols. Industry consortia are pursuing open formats, yet consensus on data timing and failure-mode handling is not expected before 2028, slowing cross-platform scalability.

High Computational Overhead Raising Bill-Of-Materials For Non-Automotive IoT

Real-time fusion often needs 10-50 TOPS of processing. Automotive OEMs absorb USD 500-800 chips such as Nvidia Orin, but drones and smart appliances target silicon below USD 50. Texas Instruments’ AWR2944 integrates on-chip fusion acceleration, though its USD 30-40 price point still strains mass-market economics.[3] The result is a bifurcated market where premium devices adopt full multi-sensor stacks, while entry-level products revert to single-modality sensing.

Segment Analysis

By Offering: Hardware Anchors Revenue While Software Drives Margin Expansion

Hardware captured 61.73% of 2025 revenue across the sensor fusion market, reflecting the capital intensity of radar, LiDAR, camera, and IMU modules that constitute the physical sensing layer. Radar modules priced between USD 50 and USD 150 dominate ADAS because of robust all-weather capabilities, whereas solid-state LiDAR, still above USD 500 per unit, is reserved for Level 3-5 programs requiring redundancy. Imaging sensors benefit from smartphone-scale economies, enabling multi-camera arrays at sub-USD 10 each. The sensor fusion market size attributed to hardware is set to increase steadily but at a slower pace than software.

Software is projected to outpace hardware with a 12.68% CAGR through 2031 as OEMs shift to over-the-air feature unlocks and subscription models. Platforms such as Mobileye SuperVision charge licensing fees per vehicle, converting one-off hardware sales into recurring revenue. ISO 26262 validation tools further enhance margins, with automakers spending USD 5-10 million per platform to certify fusion stacks. This dynamic positions software as the prime value-capture layer inside the sensor fusion industry.

By Fusion Method: Radar-Camera Dominates But LiDAR-Camera Gains Momentum

Radar-camera pairing represented 43.56% of sensor fusion market share in 2025 by combining radar’s velocity accuracy with camera-based object classification. Continental’s ARS540 4D radar extends elevation resolution, enhancing performance in cluttered urban settings CONTINENTAL.COM. LiDAR-camera fusion, supported by sub-USD 500 solid-state units, is forecast to record the fastest 12.72% CAGR. Mercedes-Benz and Stellantis deploy Valeo’s SCALA 3 LiDAR to unlock Level 3 functions, underscoring the technology’s migration from prototypes to series production.

Three-sensor frameworks that integrate radar, LiDAR, and cameras remain niche, limited to premium robotaxi programs where redundancy trumps cost. Conversely, IMU-GPS fusion is entrenched in drones and smartphones due to minimal bill-of-materials impact. As solid-state LiDAR pricing converges with imaging-radar, mid-segment vehicles are expected to embrace hybrid approaches, expanding the sensor fusion market footprint beyond luxury tiers.

By Algorithm Type: Neural Networks Challenge Kalman Filter Primacy

Kalman filters held 37.92% share in 2025, favored for deterministic outputs that simplify functional-safety audits. Their low computational burden suits mid-range microcontrollers, preserving cost efficiency. Yet transformer-based models like BEVFormer deliver superior edge-case handling and are scaling rapidly as automotive chips surpass 200 TOPS. The sensor fusion market size linked to neural-network inference is predicted to grow fastest through 2031.

Bayesian networks provide an interpretable bridge between Kalman and deep learning, attracting applications where explainability and probabilistic reasoning are vital. GNSS-INS hybridization remains dominant in aviation and maritime markets, where centimeter accuracy justifies high-end inertial units. The algorithm landscape is diverging toward a dual-stack future that marries certifiable filters for safety-critical control with neural networks for perception and prediction.

By Application: Autonomous Driving Leads Growth Beyond ADAS Saturation

ADAS contributed 49.83% of revenue in 2025, underpinned by regulatory requirements and fleet-safety programs. However, penetration already exceeds 75% in mature markets, limiting upside. Level 3-5 autonomous platforms are forecast to achieve a 12.78% CAGR, catalyzed by UNECE WP.29 approvals for conditional-automation systems. The sensor fusion market size attributed to autonomous drive will therefore outstrip ADAS from 2028 onward.

Consumer-electronics devices, especially XR headsets, captured a rising share as on-device fusion removed reliance on cloud processing. Robotics, industrial automation, and defense collectively formed 32% of revenue, with defense applications commanding premium pricing due to mission-critical reliability standards. Cross-sector technology transfer accelerates algorithm refinement as lessons from automotive migrate to drones and AMRs.

By Vehicle Type: Passenger Cars Retain Scale While Non-Road Platforms Accelerate

Passenger cars generated 62.48% of 2025 revenue and remain the volume anchor for the sensor fusion market. Euro NCAP mandates and China’s new-energy policies drive multi-sensor adoption even in sub-USD 20,000 segments. Light commercial vehicles follow, propelled by e-commerce fleets demanding driver-assist features to reduce collision insurance premiums.

Other autonomous vehicles, including delivery robots and agricultural equipment, are projected for the highest 12.73% CAGR. Labor shortages and rising input costs justify the USD 10,000-50,000 sensor-suite investment per unit. Heavy commercial trucks and buses trail adoption due to disparate regional regulations, but forthcoming U.S. and European mandates on blind-spot and cross-traffic alerts will narrow the gap by 2029.

Geography Analysis

Asia Pacific generated the largest regional revenue in 2025 at 40.81%, anchored by China’s aggressive ADAS penetration, Japan’s robotics ecosystem, and South Korea’s semiconductor supply chain. China alone accounted for 58% of regional turnover, driven by BYD, Geely, and NIO standardizing multi-sensor suites across their electric vehicles. Government incentives that tie subsidies to Level 2 functionality further expand uptake, while domestic LiDAR capacity strengthens price competitiveness for local automakers.

Europe accounted for a fair share of global revenue in 2025, benefiting from stringent Euro NCAP and General Safety Regulation mandates that require multi-modal sensing. Germany led regional demand, with Volkswagen, BMW, and Mercedes-Benz integrating fusion stacks at the platform level to amortize R&D across multiple brands. The sensor fusion market in Europe is projected to maintain steady growth as regulatory scope broadens to include commercial vehicles and motorcycles by 2028.

North America held a considerable share in 2025, driven by U.S. automakers’ voluntary ADAS commitments and allocations of 5.9 GHz V2X spectrum. The Middle East, though smaller today, is forecast for the fastest 12.75% CAGR through 2031 as the United Arab Emirates and Saudi Arabia channel defense budgets into autonomous assets requiring robust fusion. South America and Africa collectively captured a small share of revenue, constrained by lower vehicle ownership and limited LiDAR supply chains, yet mining and agriculture automation is opening targeted opportunities.

Competitive Landscape

The sensor fusion market is moderately concentrated; the top 10 vendors controlled roughly more than half of the 2025 revenue. Bosch, Continental, and Valeo leverage vertically integrated radar, camera, and middleware portfolios to win turnkey platform deals with legacy automakers seeking fast-track compliance. NXP, Infineon, and STMicroelectronics compete via automotive-grade processors that bundle ISO 26262-certified software, while Nvidia and Qualcomm focus on high-performance compute for Level 3-5 autonomy, where 200-plus TOPS throughput is mandatory.

Start-ups such as Arbe Robotics and LeddarTech are unbundling hardware and software, allowing smaller OEMs to mix-and-match sensors without vendor lock-in. Arbe’s 4D imaging radar offers LiDAR-level point-cloud density at one-third the cost, securing 2026 design wins with Chinese brands. LeddarTech’s software-defined LiDAR decouples perception algorithms from hardware, enabling automakers to switch suppliers without major code rewrites. Patent filings rose 18% year-over-year in 2024, with neural-network fusion, failure-mode isolation, and multi-modal calibration as hot areas, underscoring accelerating innovation cycles.

Strategic collaborations intensified through 2025. Valeo partnered with Qualcomm to integrate SCALA 3 LiDAR onto Snapdragon Ride Flex, targeting turnkey Level 3 solutions. Nvidia’s Orin chip locked in 25 automaker programs spanning Level 2+ to Level 3, while Renesas launched the ASIL-D-qualified R-Car V4H to serve Japanese and European OEMs. Funding rounds, such as Arbe’s USD 75 million Series C led by Temasek, signal investor confidence in imaging-radar pathways that reduce dependence on expensive LiDAR.

Recent Industry Developments

• June 2025: Nvidia disclosed 25 automaker design wins for its Orin SoC supporting 254 TOPS neural-network throughput.
• May 2025: Arbe Robotics closed a USD 75 million Series C round to scale production of its 4D imaging radar.
• April 2025: Continental introduced the ARS540 4D radar with 300 m range and on-chip fusion acceleration.
• March 2025: BYD extended its Eye of God multi-sensor system to 21 models priced below USD 25 000.

List of Companies Covered in this Report:

• Robert Bosch GmbH
• Continental AG
• NXP Semiconductors N.V.
• STMicroelectronics N.V.
• Infineon Technologies AG
• Texas Instruments Inc.
• Nvidia Corporation
• Qualcomm Incorporated
• Analog Devices Inc.
• Mobileye Global Inc.
• Aptiv PLC
• Renesas Electronics Corporation
• Valeo S.A.
• ZF Friedrichshafen AG
• Arbe Robotics Ltd.
• BASELABS GmbH
• LeddarTech Inc.
• TDK Corporation
• Kionix Inc. (ROHM)
• Memsic Inc.
• CEVA Inc.
• AMD Xilinx

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

Table of Contents

1 INTRODUCTION

1.1 Study Assumptions and Market Definition
1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

4.1 Market Overview
4.2 Market Drivers
4.2.1 Mandate of Sensor Fusion for Euro NCAP 5-Star Ratings Accelerating European OEM Adoption
4.2.2 Solid-State LiDAR Cost Decline Enabling Multi-Sensor Suites in Mid-Segment Cars across China
4.2.3 Edge-AI Chip Advancements Allowing Real-Time Multi-Modal Fusion in Mobile and XR Devices
4.2.4 Deployment of AMR Robots in Smart Factories Demanding High-Accuracy Sensor Fusion
4.2.5 Defense Modernization Programs Funding Multi-Sensor Targeting and Navigation Systems in Middle East
4.2.6 Integration of V2X Data Streams into Fusion Stacks to Unlock L4 Autonomous Driving in the United States
4.3 Market Restraints
4.3.1 Lack of Uniform Fusion Architecture Standards Hindering Interoperability
4.3.2 High Computational Overhead Raising BoM for Non-Automotive IoT Devices
4.3.3 Limited LiDAR Penetration in Emerging Markets Restricts Multi-Modal Fusion Adoption
4.3.4 Data-Privacy and Cyber-Security Concerns Around Cloud-Aided Sensor Fusion Pipelines
4.4 Value Chain Analysis
4.5 Regulatory or Technological Outlook
4.5.1 Technology Evolution Roadmap for Multi-Sensor Fusion Platforms
4.5.2 Edge-AI Integration and SoC Advancements
4.6 Impact of Macroeconomic Factors on the Market
4.7 Porter’s Five Forces Analysis
4.7.1 Bargaining Power of Suppliers
4.7.2 Bargaining Power of Buyers, Consumers
4.7.3 Threat of New Entrants
4.7.4 Threat of Substitute Products
4.7.5 Intensity of Competitive Rivalry
4.8 Key Market Trends
4.8.1 Key Patents and Research Activities
4.8.2 Major and Emerging Applications
4.8.2.1 Adaptive Cruise Control (ACC)
4.8.2.2 Autonomous Emergency Braking (AEB)
4.8.2.3 Electronic Stability Control (ESC)
4.8.2.4 Forward Collision Warning (FCW)
4.8.2.5 Other Emerging Applications

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

5.1 By Offering

5.1.1 Hardware
5.1.2 Software

5.2 By Fusion Method

5.2.1 Radar + Camera Fusion
5.2.2 LiDAR + Camera Fusion
5.2.3 Radar + LiDAR Fusion
5.2.4 IMU + GPS Fusion
5.2.5 3-Sensor Fusion (Camera + Radar + LiDAR)

5.3 By Algorithm Type

5.3.1 Kalman Filter (EKF, UKF)
5.3.2 Bayesian Networks
5.3.3 Neural Network, Deep Learning
5.3.4 GNSS, INS Integration

5.4 By Application

5.4.1 Advanced Driver Assistance Systems (ADAS)
5.4.1.1 ACC
5.4.1.2 AEB
5.4.1.3 ESC
5.4.1.4 FCW
5.4.1.5 Lane-Keep Assist (LKA)
5.4.2 Autonomous Driving (Level 3-5)
5.4.3 Consumer Electronics (AR, VR, Smartphones, Wearables)
5.4.4 Robotics and Drones
5.4.5 Industrial Automation and Smart Manufacturing
5.4.6 Defense and Aerospace

5.5 By Vehicle Type

5.5.1 Passenger Cars
5.5.2 Light Commercial Vehicles
5.5.3 Heavy Commercial Vehicles
5.5.4 Other Autonomous Vehicles

5.6 By Geography

5.6.1 North America

5.6.1.1 United States
5.6.1.2 Canada
5.6.1.3 Mexico

5.6.2 Europe

5.6.2.1 Germany
5.6.2.2 United Kingdom
5.6.2.3 France
5.6.2.4 Italy
5.6.2.5 Spain
5.6.2.6 Rest of Europe

5.6.3 Asia-Pacific

5.6.3.1 China
5.6.3.2 Japan
5.6.3.3 South Korea
5.6.3.4 India
5.6.3.5 Rest of Asia-Pacific

5.6.4 South America

5.6.4.1 Brazil
5.6.4.2 Argentina
5.6.4.3 Rest of South America

5.6.5 Middle East

5.6.5.1 Saudi Arabia
5.6.5.2 United Arab Emirates
5.6.5.3 Turkey
5.6.5.4 Rest of Middle East

5.6.6 Africa

5.6.6.1 South Africa
5.6.6.2 Nigeria
5.6.6.3 Egypt
5.6.6.4 Rest of Africa

6 COMPETITIVE LANDSCAPE

6.1 Market Concentration
6.2 Strategic Moves
6.3 Market Share Analysis
6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank, Share, Products and Services, Recent Developments)
6.4.1 Robert Bosch GmbH
6.4.2 Continental AG
6.4.3 NXP Semiconductors N.V.
6.4.4 STMicroelectronics N.V.
6.4.5 Infineon Technologies AG
6.4.6 Texas Instruments Inc.
6.4.7 Nvidia Corporation
6.4.8 Qualcomm Incorporated
6.4.9 Analog Devices Inc.
6.4.10 Mobileye Global Inc.
6.4.11 Aptiv PLC
6.4.12 Renesas Electronics Corporation
6.4.13 Valeo S.A.
6.4.14 ZF Friedrichshafen AG
6.4.15 Arbe Robotics Ltd.
6.4.16 BASELABS GmbH
6.4.17 LeddarTech Inc.
6.4.18 TDK Corporation
6.4.19 Kionix Inc. (ROHM)
6.4.20 Memsic Inc.
6.4.21 CEVA Inc.
6.4.22 AMD Xilinx

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

7.1 White-Space and Unmet-Need Assessment

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