# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
###### 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. accuracy; connected autonomous vehicles; deep learning classifiers; road detection; superpixel generation; two-level fusion of classifiers +
Footnote †: journal: Applied  
Article  
Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles  
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the environment is essential to achieve safe driving on highways and complex inner-city roads [9,10]. Road detection is categorized into structured and unstructured types based on the scenic environment. On a structured road, the detection of the road is achieved using lane detection due to road edges or well-painted markings. However, an unstructured road without any edges and road markings causes issues while detecting the region of the road [11,12]. The following features affect the road detection performance: poor adaptability, variations in illumination, diverse road environments, obstacles and no versatile and low-cost approaches [13]. Moreover, existing road detection research has been fully supervised, which requires a huge amount of labeled training data for identification purposes. This is considered to be the main disadvantage when detecting roads [14]. However, developed road detection methods should achieve high accuracy to satisfy the requirements of autonomous driving [15]. In this paper, a two-level fusion of classifiers is presented to accomplish the precise road detection. The merits combined in the TLFC are given as follows: LeNet-5 extracts the internal features without excessive rounds of training, Long Short-Term Memory (LSTM) avoids the issues of gradient explosion and gradient disappearance, and the residual block used in the residual network (ResNet) is used to resolve the degradation issue while extracting the features. Further, the SVM uses the fused feature maps from the LeNet-5, LSTM and ResNet to achieve the better classification of roads. The major contributions of this research paper are summarized as follows:  
* The KITTI and Cambridge-Driving Labeled Video Database (CamVid) datasets are used in this TLFC-RD method to evaluate the performances of the approaches. Here, superpixel generation-based pre-processing is used in the images to group similar pixels into superpixels and thus minimize the classification complexity.