# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 4 Proposed TLFC-RD Method
### Feature Extraction from the Pre-Processed Image  
The classification between the drivable and non-drivable areas is obtained by extracting appropriate features from the pre-processed image \((PI)\). In this TLFC-RD method, seven appropriate feature extraction methods are used to extract the features. The utilized feature extraction methods are the spatial values of pixels, the RGB value of pixels, entropy, HSV color space, texton features, local distance distribution and LBP. Here, the features of the spatial value, RGB value and HSV color space are used in the TLFC to categorize the background from the shadows over the road. Specifically, the spatial value of the pixels is considered to identify the road from the background mainly for the sunlight region images. Next, the HSV value accomplishes an important role when the TLFC-RD is used for dark regions. The texton feature is used to define the local structure of the images and provides the features of intersections, corners and line terminators. However, the uncontrolled lighting conditions outdoors and unpredictable weather have a great impact on region detection. The local texture of images extracted using the LBP is used to overcome the aforementioned limitation. The feature extraction process is as follows:  
1. Spatial and RGB value of pixels  
Initially, the spatial value of the pixels--i.e., \(x\) and \(y\) coordinates of pixels--are taken from the input image. Subsequently, the \(RGB\) values of the pixels are taken as feature sets from the image. 2. Entropy  
The information source that exists in the image is defined by using information or Shannon entropy. This entropy value also defines the global features of the source in an average manner. Similarly, the image entropy is calculated using the histogram of the image, because it effectively provides the complex degrees of grey value distribution. Equation (4) shows the entropy value of the input image \((I)\) by the size of \(M\times N\). \[E=-\sum_{i=0}^{t-1}p_{i}\cdot\log_{2}p_{i},\ p_{i}=\frac{n_{i}}{M\times N} \tag{4}\]  
where the probability density function and number of pixels for the gray level \(i\) are \(p_{i}\) and \(n_{i}\) respectively. 3. HSV color space  
The main advantage of using HSV in road detection is that it is identical to the human conceptual understanding of colors. Moreover, it can divide achromatic and chromatic components. Here, the color is differentiated by using the hue \((H)\), the percentage of the white light included in the pure color is denoted as the saturation \((S)\) and the perceived  
Figure 3: Pre-processed image. light intensity is denoted as \((V)\). Equations (5)-(7) express \(H,~{}S\) and \(V\), which are the components of the HSV color space. \[H=\cos^{-1}\left\{\frac{1/2[(R-G)+(R-B)]}{\sqrt{(R-G)^{2}+(R-B)(G-B)}}\right\} \tag{5}\]  
\[S=1-\frac{3}{R+G+B}[min(R,~{}G,B)] \tag{6}\]  
\[V=\frac{1}{3}(R+G+B) \tag{7}\]  
(d) Texton features  
In general, the texton features are the output acquired from the filter bank. Here, the filter bank has Gaussians in different scales such as \(k,2k\) and \(4k\). After converting the input image \((I)\) into the Lab color space, the Gaussian filters are applied in the \(R,~{}G\) and \(B\) channels. Subsequently, the 18-dimension vector of the texton feature \((TF)\) is acquired from each pixel of the image. (e) Local distance distribution  
The neighborhood space \(\eta\) of a pixel is divided into the grid of \(M\times N\times K\), which is a three-dimensional vector. Next, the distribution histogram \((DH_{t})\) for a point \(i\) at \(cell_{t}\) is expressed according to Equation (8):  
\[DH_{t}=\frac{\sum_{k\in cell_{t}}D_{ik}}{\sum_{j\in q}D_{ij}} \tag{8}\]  
where \(t=\{1,2,\ldots,~{}M\times N\times K\}\) and \(K\) is a third-dimensional vector. Hence, the resultant feature of the \(M\times N\times K\) vector is \(DH=\{DH_{1},~{}DH_{2},\ldots,DH_{M\times N\times K}\}\). (f) Local binary pattern  
In LBP, a neighborhood around each pixel is considered to generate a binary number for each pixel.