The sensor fusion market is projected to grow from USD 8.0 billion in 2023 to USD 18.0 billion by 2028, registering a CAGR of 17.8% during the forecast period.
Growing demand for
advanced driver assistance systems (ADAS) and deployment of autonomous
vehicles, increasing demand for smart homes and buildings, growing trend of miniaturization
in electronics, and increasing demand for integrated sensors in smartphones are
expected to propel the market in the next five years. However, calibration
across multiple sensors and security and safety concern are likely to pose
challenges for the industry players.
Driver: Increasing
demand for integrated sensors in smartphones
Consumer electronics is
a rapidly changing and dynamic industry with increasing competitiveness among
the market players and game-changing technological developments. Latest developments
in mobile computing and sensor technologies are making smartphones and tablets
popular worldwide.
In the present market
scenario, smartphone manufacturers are under pressure to bring exclusive and
differentiated products. Thus, if a manufacturer successfully offers a unique
product to the customers, that product will gain popularity and tend to create
a trend in the market. There is a growing trend of integrating various sensors
in smartphones. Thus, innovation and development in the sensor fusion technology
would assist the smartphones manufacturers in enhancing the features of their
products. The 9-axis inertial sensor is one of the common sensor fusion
solutions that are being integrated into smartphones for detecting the location
of the user.
Restraint: Calibration
across multiple sensors
In order to ensure that
the data from various sensors are reliable, precise, and consistent with one
another, sensor calibration is a crucial stage in the sensor fusion process.
Incorrect or inaccurate sensor fusion outcomes may emerge from sensor data that
contains errors, noise, and inaccuracies as a result of improper sensor
calibration.
The requirement for
calibration across numerous sensors, each with its own unique characteristics
and properties, is an important challenge in sensor fusion. Differences in the
sensitivity, resolution, or noise levels of various sensors may impact the
accuracy and consistency of the sensor data. To address these challenges,
sensor fusion systems need to incorporate sophisticated algorithms and
techniques that can account for sensor variability and adapt to changes in
sensor performance over time. This requires a deep understanding of sensor
characteristics and properties, as well as expertise in signal processing,
machine learning, and statistical analysis. Thus, the lack of proper
calibration across multiple sensors is acting as a restrain for the market.
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Opportunities: Growing
demand for advanced driver assistance systems (ADAS) and deployment of
autonomous vehicles
The advanced driver
assistance systems depend on the sensing capabilities provided mainly by the
combo inertial and image sensors. The MEMS inertial combo sensors are used for
the detection of the state of motion in the vehicles. The increasing use of the
GPS-IMU (Inertial Measurement Unit) fusion principle and advancements in ADAS
are assisting in resolving dead reckoning interval accumulation errors with
absolute location measurements. Using information gathered from a
forward-facing camera, Tesla's Autopilot automatic driving feature is an
example of an ADAS that can carry out tasks like maintaining the vehicle's
center in a highway lane and directing the vehicle accordingly. Since many of
the functionalities introduced within the ADAS require sensor fusion systems,
the growing deployment of ADAS is anticipated to have a positive impact on the
sensor fusion market.
Challenges: Security and
safety concern
The sensor fusion market
involves integrating data from multiple sensors to obtain a more accurate and
comprehensive understanding of the environment. While this technology has many
benefits, it also raises security and safety concerns, including: Privacy:
Sensor fusion systems have the potential to gather private information that
might be misused, such as location data, health status data, or biometric data.
It is critical to ensure that users' privacy is maintained and that data is
collected and stored securely.
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