# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 2 Related Work  
The existing road detection research along with its limitations are described in this section. [PERSON] et al. [16] achieved road segmentation and lane recognition by using a normal map. Initially, depth information was used to create a normal map. Subsequently, a normal map was used to obtain the road pavement without any buildings and vehicles. The markings of the lane were improved based on the adaptive threshold segmentation method and denoising procedures. Moreover, the starting point of the lane was identifiedby integrating the vanishing point and using a Hough transform. The high-precision depth map was used to mitigate the interference of vehicles and buildings. However, this normal map-based road detection approach was only suitable for structured roads. [PERSON] et al. [17] developed the architecture of a deep network for detecting roads. The developed deep network was the combination of DAM and the network U-Net-prior; U-Net-prior was generally a modified segmentation network that integrated the shape and location before U-Net. Next, the DAM model was used to reduce the gap between the training and test images. The accuracy of the detection was enhanced based on the off-line prior used in the detection. However, the variations in the environment created an impact on the performances of U-Net-DAM. [PERSON] et al. [18] presented the Two-Scale Fully Convolutional Network (TFCN) to identify the regions of roads. The deep learning model was enhanced by using the data acquired from the various scales. Here, the high-level semantic information and low-level details were fused using the fully convolutional layers and a skip-architecture in the two-scaled model. The multi-scale feature maps achieved from the various reception fields were used to identify the road areas. Moreover, the feature map was used to eliminate the redundancies of the scale information. This TFCN was analyzed only in the low-level information, and the lesser intersection over the union affected the accuracy of road detection. [PERSON] et al. [19] developed the hierarchical multi-feature road image segmentation framework (HMRF) to obtain road target data. A superpixel method, namely the Simple Linear Iterative Clustering (SLIC) algorithm, was used to obtain the local feature subregions from the image. The features obtained using the SLIC algorithm preserved the boundary information as it is used to minimize the incidence of over-segmentation. Next, the ensemble-learning random-forest method was used to classify the road environment. This HMRF failed to analyze larger datasets for road detection. [PERSON] et al. [20] presented the cooperative machine learning approach TAAWUN to achieve road detection for connected autonomous vehicles. TAAWUN was used as a binary classification, where TAAWUN was used to classify either the road or background class labels. Moreover, the developed TAAWUN has two variations: a hard majority vote (MV) and soft majority vote (MVP). In a challenging environment, the problem-specific feature sets were used to improve the accuracy of the TAAWUN method. TAAWUN was used with only a small number of features during detection as it provided the final results based on voting, which may lead to inappropriate classification. [PERSON], [PERSON] et al. [21] developed the LiDAR-camera fusion strategy to exploit color and range data during road detection. In this work, discrete 3D LiDAR points were converted into 2D LiDAR range images at the LiDAR. A faster road estimation was achieved by using the distance-aware height-difference based scanning method. Next, the Multi-Modal Conditional Random Field (MM-CRF) was used, fusing the binary and dense results of road detection. However, this MM-CRF was processed only in the urban road condition. [PERSON], [PERSON] et al. [22] presented an end-to-end road segmentation network, namely the Spatial Propagation and Spatial Transformation Fusion Network (SPSTFN), that considered the merits of the multi-modal data fusion and deep network.