Automotive Sensor Fusion Market Competitive landscape By 2024

The technique of combining data from numerous sensors to calculate accurate position and alignment information to perceive the environment more accurately is called sensor fusion. In an automotive context, it is called automotive sensor fusion. Automotive sensor fusion relies on the correct sensor combination.

This software improves accuracy, enhances context awareness, enables gesture detection, and enables auto calibration. Advance driver assistance systems (ADAS) is the outcome of automotive sensor fusion. Automotive suppliers are gazing to use this technology to boost their growth in the fast-growing ADAS and automated driving market.

The automotive sensor fusion market has been perceiving substantial growth across the globe, due to increasing demand for advanced driver assistance systems (ADAS), integrated sensors in smartphones, and growing trends of miniaturization in electronics.

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Moreover, electronic devices such as wearable devices, smartphones, gaming consoles, and smart TVs are the major drivers of the automotive sensor fusion market. Stringent regulations on vehicle emissions are some of the central factors likely to be the major automotive sensor fusion market driver.

However, the high initial cost of deployment and the lack of standardization in memory devices and automotive sensor fusion systems are expected to hinder the market’s growth during the forecast period.

The need for commendable driving automation, increasing demand for the Internet of Things (IoT), is expected to create great opportunities for automotive manufacturers the world over.

Furthermore, the use of sensors in the design of advanced planes and high-tech aerial drones is likely to expand the growth opportunities for automotive sensor fusion market during the forecast period.

The global automotive sensor fusion market is broadly segmented by sensor type and by sensing parameters, by application, by vehicle type, by industry verticals, and by region. On the basis of sensor type and sensing parameters, the automotive sensor fusion market is further classified into temperature sensors, pressure sensors, position sensors, oxygen sensors, NOx sensors, speed sensors, inertial sensors, and image sensors.

Inertial sensors are further classified into accelerometers and gyroscopes. Image sensors are again classified into CMOS and CCD sensors. Others are radar sensors, ultrasonic sensors, rain sensors, relative humidity sensors, proximity sensors, and particulate matter sensors. Vehicle type is the key factor in segmenting the global market into passenger car, LCV, and HCV vehicles segments.

Based on application the market is subdivided into the power train sensors market, the chassis sensors market, the exhaust sensors market, the safety and control sensors market, the body electronics sensors market, and the telematics sensors market. It is also segmented on the basis of geographical regions into Middle East and Africa (MEA), Asia-Pacific (APAC), North America, Europe, and South America.

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The market in North America is expected to hold the major share of the global market, primarily due to the developed economies and high adoption of technological advanced systems and stringent regulations on vehicle’s emissions are also responsible for the market’s expected growth during the forecast period. Also, APAC is likely to hold great opportunities for this market during the forecast period due to the massive technological advancements expected to come its way.

The key participants of the automotive sensor fusion market are Robert Bosch GmbH, Continental AG, Allegro Microsystems, Delphi Automotive PLC, Denso Corporation, Infineon Technologies AG, Sensata Technologies Inc., Elmos Semiconductor AG, CTS Corporation, Autoliv Inc., Freescale Semiconductor, Inc., and General Electric Co.

The report offers a comprehensive evaluation of the market. It does so via in-depth qualitative insights, historical data, and verifiable projections about market size. The projections featured in the report have been derived using proven research methodologies and assumptions.

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