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

Applied sciences, 2021-09, Vol.11 (17), p.7984 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2076-3417 ;EISSN: 2076-3417 ;DOI: 10.3390/app11177984

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  • Title:
    Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
  • Author: Subramani, Prabu ; Sattar, Khalid ; de Prado, Rocío ; Girirajan, Balasubramanian ; Wozniak, Marcin
  • Subjects: Accuracy ; Autonomous vehicles ; Classification ; Classifiers ; connected autonomous vehicles ; Deep learning ; deep learning classifiers ; Environmental impact ; Learning algorithms ; Localization ; Machine learning ; road detection ; Roads ; Roads & highways ; Sensors ; superpixel generation ; two-level fusion of classifiers ; Vehicles ; Wireless communications
  • Is Part Of: Applied sciences, 2021-09, Vol.11 (17), p.7984
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2076-3417
    EISSN: 2076-3417
    DOI: 10.3390/app11177984
  • Source: ProQuest Databases
    ROAD: Directory of Open Access Scholarly Resources
    DOAJ Directory of Open Access Journals
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