Research on Multi - Sensor Fusion Technology of IMU LiDAR and GNSS for Autonomous Driving

With the remarkable development of the practicality of deep learning and the ultra - high - speed information transmission rate of 5G communication technology, autonomous driving is becoming a key technology that affects future industries. Sensors are crucial for perceiving the external world in aut...

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Bibliographic Details
Published in:	2025 5th International Conference on Sensors and Information Technology pp. 407 - 410
Main Author:	Cheng, Zexiang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 21.03.2025
Subjects:	Adaptation models Autonomous vehicles Global navigation satellite system Laser radar Machine learning algorithms Machine learning models Multi - sensor fusion algorithms Predictive models Random forests Sensor fusion Sensor systems Sensors
Online Access:	Get full text
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Summary:	With the remarkable development of the practicality of deep learning and the ultra - high - speed information transmission rate of 5G communication technology, autonomous driving is becoming a key technology that affects future industries. Sensors are crucial for perceiving the external world in autonomous driving systems, and their performance influences the safety of autonomous vehicles. In this study, we deeply explore the multi - sensor fusion technology of IMU, LiDAR and GNSS for autonomous driving. A fusion architecture and algorithm are designed based on the Extended Kalman Filter. The predicted position state curve of the vehicle after incorporating the machine learning model is discussed. It is concluded that the sensor fusion technology after integrating the Random Forest model algorithm can more precisely predict the position state of the vehicle.
DOI:	10.1109/ICSI64877.2025.11009780