Vehicle Classification Using Combined Laser Rangefinder and Pyroelectric Infrared Sensors in a Real Dynamic Environment

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Published: Dec 27, 2025
Keywords:
Vehicle classification, Laser Rangefinder, Pyroelectric Infrared, neural network, dynamic environments.

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Vu Van Quang
Precision Engineering & Smart Measurements Lab, School of Mechanical Engineering; Hanoi University of Science and Technology, No 1 Dai Co Viet Street, Bach Mai Ward, Hanoi 100000, Vietnam
Nguyen Van Tuan
Precision Engineering & Smart Measurements Lab, School of Mechanical Engineering; Hanoi University of Science and Technology, No 1 Dai Co Viet Street, Bach Mai Ward, Hanoi 100000, Vietnam
Nguyen Nam Anh
Precision Engineering & Smart Measurements Lab, School of Mechanical Engineering; Hanoi University of Science and Technology, No 1 Dai Co Viet Street, Bach Mai Ward, Hanoi 100000, Vietnam
Nguyen Tien Dung
Precision Engineering & Smart Measurements Lab, School of Mechanical Engineering; Hanoi University of Science and Technology, No 1 Dai Co Viet Street, Bach Mai Ward, Hanoi 100000, Vietnam
Vu Toan Thang
Precision Engineering & Smart Measurements Lab, School of Mechanical Engineering; Hanoi University of Science and Technology, No 1 Dai Co Viet Street, Bach Mai Ward, Hanoi 100000, Vietnam
Bui Hai Dang
Faculty of Information Technology, University of Transport Technology, No 54 Trieu Khuc Street, Thanh Liet Ward, Hanoi 100000, Vietnam; Precision Engineering & Smart Measurements Lab, School of Mechanical Engineering; Hanoi University of Science and Technology, No 1 Dai Co Viet Street, Bach Mai Ward, Hanoi 100000, Vietnam

Abstract

This paper presents a vehicle classification approach for real-world dynamic environments based on sensor fusion between a laser rangefinder (LRF) and a pyroelectric infrared (PIR) sensor. By integrating geometric shape information from the LRF with thermal distribution patterns captured by the PIR sensor, the system extracts distinctive features that effectively suppress noise introduced by external environmental variations. A lightweight neural network is developed for classification, achieving a minimum accuracy of 91% for specific vehicle types and an average accuracy of 94% across all categories. Owing to its high accuracy and low computational cost, the proposed model is well-suited for implementation in portable embedded platforms, functioning as intelligent measurement nodes within Intelligent Transportation Systems (ITS).

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