# A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
## 4 Applications Overview of Deep Learning in the Field of Self-Driving Cars
### Lane Recognition

Table 3: A summary of the deep learning method-based scene recognition algorithms. To deal with the disadvantages of the traditional image processing methods in lane detection, many scholars have applied deep learning to lane recognition. For example, [PERSON] et al. [67] proposed a lane detection algorithm, where the semantic road lane is estimated using the regression and classification framework based on the extra tree. The input of this framework is extracted from the deconvolutional network. The training and testing process of the method proposed in [67] is shown in Figure 16.  
As shown in Figure 16, in the training phase of the method in [67], Stage A is used to fine-tune the deconvolution network. The input of the deconvolution network is the combination of the hue, saturation, and depth (HSD). This deconvolution network is trained to extract the features of the road scene. Stage B is used to train the extra trees-based classifier for modeling the relationship between the road scene and the labels. Then this classifier is used to predict the scene labels based on the color and depth features of the road scenes. In Stage C, for each road scene label, a separate extra tree regressor is trained using the image-based deep features and the lane marker locations annotated manually. This extra tree regressor can be used to predict the lane marker position. In the testing phase of the method in [67] (see Figure 16), the trained deconvolution network is used to predict the road surface and output the features of the road surface. Then, these features are input to the classifier model to get the scene label. At last, the estimated scene labels and the road features are input to the corresponding regression model, to predict the lane locations and the semantic information. Results of the algorithm proposed in [67] are shown in Figure 17.  
Figure 16: The training and testing process of the method in [67]. In order to deal with complex noises and scenes in the lane detection for self-driving cars, lots of methods based on deep learning have been proposed. For example, [PERSON], et al. [68] proposed an accurate and fast deep CNN, which combined self-attention and channel attention in lane marking detection. [PERSON], et al. [69] proposed a stacked ELM (extreme learning machine) architecture for CNNs, which was applied to lane detection. This method can reduce learning time and produce accurate results. [PERSON] [70] designed a gradient-guided deep convolution network to detect the presence of lane, where the gradient cues and geometric attributes are used. In this method, the spatial distribution of detected lanes is represented by a recurrent neural layer. Lane detection based on deep learning can be divided into two categories: one-stage method and two-stage method. The one-stage method refers to the method that directly outputs the parameters about the lane through the deep network. The two-stage method means that it is divided into two steps: Firstly, semantic segmentation is carried out through the deep network to output the pixel collection of the lanes; Secondly, a curve through these pixels is fitted to get the lane parametrization. A summary of the lane detection methods based on deep learning is illustrated in Table 4.