Design of Vehicle Unmanned Driving Navigation System

This paper studies the design of unmanned vehicle navigation system, aiming to improve the autonomous navigation capability of unmanned vehicles in complex environments. Based on multi-sensor information fusion technology, combined with positioning navigation algorithm and dynamic path planning method, a high-precision and high-real-time unmanned navigation system is constructed. During the system design process, key technologies such as visual SLAM, inertial navigation and GPS fusion positioning are studied, and a target recognition and environment perception module based on deep learning is designed to realize the autonomous decision-making and path planning of vehicles in dynamic environments. Through testing and verification in actual scenarios, the system performs well in navigation accuracy, obstacle avoidance ability and path planning efficiency in complex environments. The research results show that the proposed unmanned navigation system has good adaptability and reliability in scenarios such as urban roads and highways. What’s more, this paper shows that there are more and more theoretical support and practical reference for the application of unmanned driving technology in the future.

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Publication Details

Journal
MATEC Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/matecconf/202541004005
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

Design of Vehicle Unmanned Driving Navigation System

Yuanfu Zhang
MATEC Web of Conferences
Robotic Path Planning Algorithms
article

Design of Vehicle Unmanned Driving Navigation System

Yuanfu Zhang
article en

Abstract

This paper studies the design of unmanned vehicle navigation system, aiming to improve the autonomous navigation capability of unmanned vehicles in complex environments. Based on multi-sensor information fusion technology, combined with positioning navigation algorithm and dynamic path planning method, a high-precision and high-real-time unmanned navigation system is constructed. During the system design process, key technologies such as visual SLAM, inertial navigation and GPS fusion positioning are studied, and a target recognition and environment perception module based on deep learning is designed to realize the autonomous decision-making and path planning of vehicles in dynamic environments. Through testing and verification in actual scenarios, the system performs well in navigation accuracy, obstacle avoidance ability and path planning efficiency in complex environments. The research results show that the proposed unmanned navigation system has good adaptability and reliability in scenarios such as urban roads and highways. What’s more, this paper shows that there are more and more theoretical support and practical reference for the application of unmanned driving technology in the future.

MATEC Web of ConferencesVol. 410
Beijing University of Technology (CN), University of Science and Technology Beijing (CN)
Openalex Percentile: Top 84%
Robotic Path Planning Algorithms
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