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Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles

Authors :
Prabu Subramani
Khalid Nazim Abdul Sattar
Rocío Pérez de Prado
Balasubramanian Girirajan
Marcin Wozniak
Source :
Applied Sciences, Vol 11, Iss 17, p 7984 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Connected autonomous vehicles (CAVs) currently promise cooperation between vehicles, providing abundant and real-time information through wireless communication technologies. In this paper, a two-level fusion of classifiers (TLFC) approach is proposed by using deep learning classifiers to perform accurate road detection (RD). The proposed TLFC-RD approach improves the classification by considering four key strategies such as cross fold operation at input and pre-processing using superpixel generation, adequate features, multi-classifier feature fusion and a deep learning classifier. Specifically, the road is classified as drivable and non-drivable areas by designing the TLFC using the deep learning classifiers, and the detected information using the TLFC-RD is exchanged between the autonomous vehicles for the ease of driving on the road. The TLFC-RD is analyzed in terms of its accuracy, sensitivity or recall, specificity, precision, F1-measure and max F measure. The TLFC- RD method is also evaluated compared to three existing methods: U-Net with the Domain Adaptation Model (DAM), Two-Scale Fully Convolutional Network (TFCN) and a cooperative machine learning approach (i.e., TAAUWN). Experimental results show that the accuracy of the TLFC-RD method for the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset is 99.12% higher than its competitors.

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
17
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.76584c24990b4d6abdb841caef6107e4
Document Type :
article
Full Text :
https://doi.org/10.3390/app11177984