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