Overview of Multi Sensor Data Fusion In Autonomous Vehicles Challenges And Solutions
Multi-sensor data fusion technology for driverless driving is one of the key technologies to realize autonomous vehicles . Driverless cars need to sense the surrounding environment in real time, including other vehicles , pedestrians, road signs, traffic lights, and the state of the road itself.
Multimodal Sensor Fusion in Autonomous Vehicles
The rapid progress of sensing technologies, artificial intelligence, and embedded computing has significantly accelerated the development of autonomous vehicles . Among the core challenges of higher-level driving automation, reliable environmental perception remains one of the most critical. This review presents a systematic PRISMA-based analysis of multimodal sensor technologies and fusion ...
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Among the core challenges of higher-level driving automation, reliable environmental perception remains one of the most critical. This review presents a systematic PRISMA-based analysis of multimodal sensor technologies and fusion architectures applied in autonomous driving, based on 66 peer-reviewed studies published between 2014 and 2025.
Integrating Multi
To overcome these challenges , multi-sensor fusion has emerged as a vital approach in autonomous driving. By integrating data from multiple sensor modalities, multi-sensor fusion provides a more comprehensive and robust understanding of the environment, enhancing the reliability and safety of autonomous systems [3].
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Modern autonomous systems in diverse domains such as autonomous vehicles and unmanned aerial systems, healthcare monitoring, and defense applications rely heavily on sensor fusion to achieve reliable operation in real-world environments [4, 5]. These systems rely on a multitude of sensors to perceive their environment and make informed decisions. The integration of sensors such as LiDAR ...
A Review of Multi
We present a comprehensive review of recent progress in multi -modal sensor fusion for autonomous driving, spanning from fusion architectures and task-specific adaptations to practical deployment challenges .
This book offers a comprehensive overview of multi-sensor fusion in autonomous driving in terms of the challenges , methods, and applications.
Key Details About Multi Sensor Data Fusion In Autonomous Vehicles Challenges And Solutions
However, the inherent limitations of single-modal sensors have necessitated a shift towards multi -modal sensor fusion . This approach leverages the unique advantages of diverse sensor systems integrated into autonomous vehicles (AVs), significantly improving perception accuracy.
By integrating data from multiple sensor modalities, multi-sensor fusion provides a more comprehensive and robust understanding of the environment, enhancing the reliability and safety of autonomous systems [3]. As the complexity of driving scenarios increases, the role of multi-sensor fusion becomes even more significant.
