Road Types Identification Method of Unmanned Tracked Vehicles Based on Fusion Features

doi:10.12382/bgxb.2022.0038

Abstract

Abstract:

Unmanned tracked vehicles often navigate challenging terrain, and incorporating road types as priori information for tasks such as suspension control, automatic gear decision, and path planning can enhance their performance. However, methods based on single-class features have limitations in accuracy and environmental adaptability. To overcome this, a road-type identification method based on fusion features is proposed, combining deep image features with the statistical features of vertical acceleration in time, frequency, and power spectral density domains. Machine learning classification algorithms are used to identify the road types. Compared to using single class of features, the proposed method using fusion features enriches image features and vertical acceleration features, and improves the accuracy and environmental adaptability. The response speed of the method based on fusion features is similar to that of the image-based methods. Five machine learning classification algorithms are compared. The results show that support vector machine and random forest are the most accurate and fastest classification algorithms, achieving over 90% accuracy with a speed of 14 frames per second.

Key words: unmanned tracked vehicles, fusion features, machine learning, convolutional neural network, road types identification

LIU Jia, LIU Hai’ou, CHEN Huiyan, MAO Feihong. Road Types Identification Method of Unmanned Tracked Vehicles Based on Fusion Features[J]. Acta Armamentarii, 2023, 44(5): 1267-1276.

Figures/Tables 10

Fig.1 Overview of the identification method for proposed road types

Fig.2 Convolutional neural network structure

Fig.3 Tracked data collection platform

Fig.4 Images and vertical acceleration data of four road types

Fig.5 Changes in accuracy and loss during training

Fig.6 Accuracy of road types identification methods based on different features

Fig.7 Confusion matrixes of road-type identification methods based on different features

Fig.8 Identification results of methods based on different features

Fig.9 Accuracy of road-type identification methods with different machine learning classification algorithms

Table 1 Time taken for identification using different machine learning classification algorithms

算法	单次识别用时/ms
SVM	2.48
ANN	0.14
DT	0.08
Bagging+SVM	44.54
RF	3.65

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