The Worldwide Sensor Fusion Industry for Automotive is Expected to Reach $22.2 Billion by 2030
Thursday, October 21, 2021
DUBLIN, Oct. 13, 2021 /PRNewswire/ -- The "Global Sensor Fusion Market for Automotive by Technology (Camera, LIDAR & RADAR), Data Fusion Type & Level (Homogeneous, Heterogeneous, Data, Decision, Feature), Software Layer, Vehicle Type (ICE, Autonomous & Electric) and Region - Forecast to 2030" report has been added to ResearchAndMarkets.com's offering.
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The ICE sensor fusion market for automotive is projected to reach USD 22.2 billion by 2030 from an estimated USD 2.9 billion in 2021, at a CAGR of 25.4% during the forecast period.
Various governments globally are implementing safety standards by making safety features such as automated emergency brake, adaptive cruise control, lane departure warning a mandatory feature in vehicles, driving adoption of cameras, radars and LiDARs in automobiles.
This is expected to strongly drive the adoption of sensor fusion in developed as well developing countries. Also, growing popularity of high-end and luxury cars is boosting sensor fusion market for automotive. Countries such as India, China, Brazil, Mexico, Argentina, European Union, US are focusing on enhanced automotive safety standards. Thus, the demand is expected to gain momentum globally.
Heterogeneous fusion type is expected to be the largest market by data fusion type
The key benefits of heterogenous sensor fusion are enhanced system performance and robustness. Examples of multi-modal fusion systems or heterogeneous sensors are visible cameras, Far IR cameras, visual cameras LASER scanner radar, GPS localizer CAN bus Gyroscope, etc. Various modern sensor networks are heterogenous - a combination of a variety of wired and wireless sensors/actuators.
For instance, in a driver assistance system, the system collects data from internal and external sensors installed in the car. This includes various types of sensors such as GPS localizers, a CAN bus, a gyroscope, radar, and cameras. Thus, the multiple benefits offered by heterogeneous sensor data fusion are driving its popularity in vehicles.
Decision fusion market segment is expected to be the fastest
In decision level sensors, each sensor makes an individual decision before forming a combination of decisions to arrive at a more informed final decision, i.e., target decision fusion. Decision fusion is less complex than data fusion. Decision-making algorithms, as a key technology for uncertain data fusion, is the core to obtain reasonable multisensory information fusion results.
Thus, there is a broad application of decision-making algorithms on target attributes, characteristics, and types through detailed processing of information obtained through various sensors. A multitude of theorems and algorithms are emerging in decision sensor fusions. Decision fusion is expected to gain popularity globally in the coming years, owing to its advantages and less complex architecture.
One of the many practical benefits offered by decision fusion is that it allows combining individual results, even if it was not expected in the testing of the algorithm. Consequently, different sources of information can be easily exchanged, and the fusion strategy is readily adapted to unknown future changes of input sources.
Asia Pacific market is expected to register the highest growth during the forecast period
The Asia Pacific sensor fusion market for automotive is estimated to be the fastest-growing regional market. The growing adoption of advanced ADAS technologies in China, Japan, South Korea, and India is expected to drive market growth in the region. China's passenger car production is expected to reach 24 million units by 2026, presenting a huge opportunity for sensor fusion hardware manufacturers and software/algorithm developers globally as well as domestically.
Not only passenger cars but trucks are also set to reach 2 million units by 2026. The South Korean transport ministry announced that it requires all new large passenger vehicles and trucks to be fitted with AEB and LDW systems from January 2019. Thus, the implementation of government mandates is expected to drive the adoption of sensors- cameras, radars and LiDARs. Such factors would in turn, drive the growth of sensor fusion technology during the forecast period.
Key Topics Covered:
1 Introduction
2 Research Methodology
3 Executive Summary 3.1 Pre & Post COVID-19 Scenario 3.2 Report Summary
4 Premium Insights 4.1 Attractive Opportunities In Sensor Fusion Market for Automotive 4.2 Sensor Fusion Market for Automotive, by Data Fusion Type 4.3 Sensor Fusion Market for Automotive, by Vehicle Type 4.4 Sensor Fusion Market for Automotive, by Technology 4.5 Sensor Fusion Market for Automotive, by Fusion Level 4.6 Sensor Fusion Market for Autonomous Vehicles, by Level of Autonomy 4.7 Sensor Fusion Market for Electric Vehicles, by Vehicle Type 4.8 Sensor Fusion Market for Automotive, by Region
5 Market Overview 5.1 Introduction 5.2 Market Dynamics 5.2.1 Drivers 5.2.1.1 Technical advantages offered by sensor fusion 5.2.1.2 Stringent emission standards regarding NOx and particulate matter 5.2.2 Restraints 5.2.2.1 Lack of standardization in software architecture/hardware platforms 5.2.3 Opportunities 5.2.3.1 Development of autonomous vehicles 5.2.4 Challenges 5.2.4.1 Security and safety concerns 5.2.5 Impact of COVID-19 On Sensor Fusion Market for Automotive 5.3 Trends/Disruptions Impacting Customer'S Business 5.4 Pricing Analysis 5.5 Value Chain Analysis 5.6 Patent Analysis 5.7 Ecosystem/Market Map 5.8 Sensor Fusion Market for Automotive, Scenarios (2019-2030) 5.8.1 Most Likely Scenario 5.8.2 High COVID-19 Impact Scenario 5.8.3 Low COVID-19 Impact Scenario 5.9 Porter'S Five forces Analysis 5.9.1 Threat of New Entrants 5.9.2 Threat of Substitutes 5.9.3 Bargaining Power of Suppliers 5.9.4 Bargaining Power of Buyers 5.9.5 Intensity of Competitive Rivalry
6 Sensor Fusion Market for Automotive, by Environment 6.1 Introduction 6.2 Internal Sensors 6.3 External Sensors
7 Sensor Fusion for Automotive: Algorithms 7.1 Introduction 7.2 Kalman Filter 7.3 Bayesian Filter 7.4 Central Limit theorem 7.5 Convolutional Neural Networks
8 Sensor Fusion Market for Automotive, by Technology 8.1 Introduction 8.1.1 Research Methodology 8.1.2 Assumptions/Limitations 8.1.3 Industry Insights 8.2 Cameras 8.2.1 Technical Advantages Such As Reading Signs & Classifying Objects Boost Demand for Cameras 8.3 Radar 8.3.1 Affordability and Clarity In Challenging Conditions Expected To Drive Radar Demand 8.4 LiDAR 8.4.1 Enhanced Obstacle Detection & Safe Navigation Boost Application In Vehicles
9 Sensor Fusion Market for Automotive, by Fusion Level 9.1 Introduction 9.1.1 Research Methodology 9.1.2 Assumptions/Limitations 9.1.3 Industry Insights 9.2 Feature Fusion 9.2.1 Accuracy of Feature Level Fusion Drives Its Popularity 9.3 Decision Fusion 9.3.1 Developments In Algorithms for Decision Fusion Boost Growth 9.4 Data Fusion 9.4.1 Lower Detection Error Probability Drives Segment Growth
10 Sensor Fusion Market for Automotive, by Vehicle Type 10.1 Introduction 10.1.1 Research Methodology 10.1.2 Assumptions/Limitations 10.1.3 Industry Insights 10.2 Passenger Cars 10.2.1 Implementation of Regulations To Make ADAS Standard In Passengers Cars Drives Segment 10.3 Light Commercial Vehicles (LCV) 10.3.1 Safety Regulations To Reduce Accidents Boosts Adoption of Sensor Fusion In Lcvs 10.4 Heavy Commercial Vehicles (HCV) 10.4.1 Segment Driven by Adoption of ADAS Features In Hcvs
11 Sensor Fusion Market for Automotive, by Data Fusion Type 11.1 Introduction 11.1.1 Research Methodology 11.1.2 Assumptions/Limitations 11.1.3 Industry Insights 11.2 Homogenous 11.2.1 Homogenous Fusion To Witness Moderate Growth During forecast Period 11.3 Heterogenous 11.3.1 Growing Demand for Premium Vehicles With Sensor Fusion Expected To Drive Demand
12 Sensor Fusion Market for Automotive, by Software Layer 12.1 Introduction 12.1.1 Research Methodology 12.1.2 Assumptions/Limitations 12.1.3 Industry Insights 12.2 Operating System 12.2.1 Ongoing Developments In Advanced Software Operating Systems Drive Popularity 12.3 Middleware 12.3.1 Availability of Various Middleware Expected To Boost Market 12.4 Application Software 12.4.1 Developments In Application Software With More Advanced Features Expected To Drive Adoption
13 Sensor Fusion Market for Electric Vehicles, by Vehicle Type 13.1 Introduction 13.1.1 Research Methodology 13.1.2 Assumptions/Limitations 13.1.3 Industry Insights 13.2 Battery Electric Vehicles (Bev) 13.2.1 Regulations To Mandate ADAS Features In Bevs Boost Segment 13.3 Plug-In Hybrid Electric Vehicles (PHEV) 13.3.1 Increasing Sales of PHEVs With ADAS Features Boost Segment 13.4 Fuel-Cell Electric Vehicles (FCEV) 13.4.1 Launch of FCEV Models With ADAS Features To Drive Growth
14 Sensor Fusion Market for Autonomous Vehicles, by Level of Autonomy 14.1 Introduction 14.1.1 Research Methodology 14.1.2 Assumptions/Limitations 14.1.3 Industry Insights 14.2 L4 14.2.1 Segment Propelled by Oem Investment In Automated Driving 14.3 L5 14.3.1 Increased Testing of Autonomous Driving Boosts Advancements In L5
15 Sensor Fusion Market for Automotive, by Region
16 Automotive Sensors Market, by Sensor Type 16.1 Introduction 16.2 Temperature Sensors 16.2.1 Temperature Sensors Mainly Used In Powertrain and HVAC Applications 16.3 Pressure Sensors 16.3.1 Pressure Sensors Mainly Used In HVAC, Safety & Control, and TPMS 16.4 Position Sensors 16.4.1 Position Sensors Widely Used To Provide Information To ECMS 16.5 Oxygen Sensors 16.5.1 Oxygen Sensors Used To Measure Proportional Amount of Oxygen In Liquid Or Gas 16.6 Nitrogen Oxide Sensors 16.6.1 Stringent Government Regulations To Limit NOx Emissions To Provide Opportunities for NOx Sensors 16.7 Speed Sensors 16.7.1 Speed Sensors Used To Measure Engine Camshaft Speed and Vehicle Speed 16.8 Inertial Sensors 16.8.1 Inertial Sensors Mainly Based On Mems Technology and Used In Accelerometers and Gyroscopes 16.8.1.1 Accelerometers 16.8.1.2 Gyroscopes 16.9 Image Sensors 16.9.1 Increasing Adoption of ADAS To Boost Use of Image Sensors 16.9.1.1 CMOS 16.9.1.2 CCD 16.10 Other Sensors 16.10.1 Radar 16.10.2 Ultrasonic Sensors 16.10.3 Rain Sensors 16.10.4 Relative Humidity Sensors 16.10.5 Proximity Sensors 16.10.6 Particulate Matter Sensors 16.10.7 LiDAR 16.10.8 Current Sensors
17 Automotive Sensors Market, by Application
18 Recommendations by the Publisher 18.1 Asia Pacific: A Potential Market for Sensor Fusion Market for Automotive 18.2 Strategic Adoption of LiDAR To Create New Revenue Pockets 18.3 Growing Demand for Sensor Fusion In Electric & Autonomous Vehicles 18.4 Conclusion
19 Competitive Landscape 19.1 Overview 19.2 Key Player Strategies/Right To Win 19.3 Revenue Analysis of Top Five Players, 2018-2020 19.4 Market Share Analysis 19.5 Competitive Leadership Mapping 19.5.1 Star 19.5.2 Emerging Leader 19.5.3 Pervasive 19.5.4 Participant 19.6 Competitive Scenario 19.7 New Product Launches 19.8 Agreements, Partnerships, Collaborations, and Joint Ventures
20 Company Profiles 20.1 Key Players 20.1.1 Robert Bosch GmbH 20.1.2 Continental AG 20.1.3 NXP Semiconductors N.V. 20.1.4 STMicroelectronics 20.1.5 ZF Friedrichshafen AG 20.1.6 Infineon Technologies 20.1.7 Allegro Microsystems 20.1.8 Denso Corporation 20.1.9 Sensata Technologies, Inc. 20.1.10 Elmos Semiconductor Se 20.1.11 TE Connectivity Ltd. 20.2 Other Key Players 20.2.1 CTS Corporation 20.2.2 Baselabs GmbH 20.2.3 Memsic Semiconductor (Tianjin) Co., Ltd. 20.2.4 Kionix, Inc. 20.2.5 TDK Corporation 20.2.6 Analog Devices 20.2.7 Microchip Technology Inc. 20.2.8 Monolithic Power Systems, Inc. 20.2.9 Leddartech Inc. 20.2.10 Ibeo Automotive Systems GmbH 20.2.11 Maxim Integrated 20.2.12 Velodyne LiDAR, Inc. 20.2.13 Renesas Electronics Corporation 20.2.14 Mobileye 20.2.15 Aptiv Plc 20.2.16 Magna International
21 Appendix
For more information about this report visit https://www.researchandmarkets.com/r/fivj9g
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