## 1 Introduction  
Thanks to the development of big data and computer vision and other advanced technology [1; 2; 3], modern cars are equipped with a large number of Advanced Driver Assistant Systems (ADASs), such as Lane Departure Warning (LDW) and Lane Keeping Assist Systems (LKAS), which are designed to perform the functions of lane departure warning and keeping [4]. However, before vehicles with these safety applications are launched on the market, numerous road tests ought to be carried out. Suggestions and basis for improvement are provided by evaluating the performance of LDW and LKAS systems [5; 6]. The performances of LDW and LKAS are related to the accurate positioning of a vehicle between two lane boundaries and the required accuracy is almost 100% [7]. The lane recognition based on machine vision is mainly composed of feature extraction and lane detection [8]. The validity of extracted features determines the accuracy of lane detection. The contour information of lane is generally extracted, then the contour information is matched by the modelling of lane, and the location of lane in the image is obtained. For example, Canny, Robert, Prewitt, and Sobel are used to find shapes in images, Gaussian filters are used to smooth images after edge detection with Canny operators, and then lanes are fitted by the Hough Transform or Random Sample Consensus (RANSAC) algorithm [9; 10; 11]. However, these edge detection algorithms not only obtain lane contour model, but also bring a lot of other noises, such as the contour of roadside buildings and driving vehicles, etc., which brings inconvenience to the subsequent denoising. Most lanes appear in the collected images in the form of straight lines, and the Hough Transform is a standard edge extractor that has significant advantages in finding lines in the image, so it is often used for lane detection. [PERSON] et al. [12] broke the problem down into several steps: create a bird-view of the road, divide pixels belonging to longitudinal road markings, use the Hough Transform to extract lane boundaries, and adjust pitch angles. In [13], the parameter equation of the line to be matched is obtained by using the Hough Transform and the lane is extracted by RANSAC verification in the straight line constraint box, which can meet the real-time requirements. The lane detection method that is based on the Hough Transform has good robustness to the straight lane, but the robustness is reduced on curved and damaged lanes. In order to enhance the feature representation of the lane, the colour information is also fully utilized. The colour information based lane detection algorithm usually converts the colour mode RGB (Red, Green, and Blue) to HSI (Hue, Saturation, and Intensity), which can greatly reduce the colour composition of the road surface when the shadow or light is insufficient [14]. In [15], the lane marking features are extracted by analysing the colour distribution of road, but the performance of the extractor will be affected by drastic illumination changes. [PERSON] et al. [16] used adaptive local thresholds to generate a binary edge image with good performance at edges. [PERSON] et al. [17] used gradient enhancement transformation to produce gray-scale images with large gradients at lane boundaries. [PERSON] et al. [18] designed a filter to extract lane markings from grayscale images. The disadvantage of these algorithms is that they are seriously affected by the brightness change of light. Additionally, other vehicles on the road often interfere with lane identification. Stereo vision [19] technology can be used to distinguish lanes and obstacles, but stereo vision requires high accuracy of camera calibration parameters and the algorithms are relatively complex. In order to reduce the effect of sensors on the detection accuracy, some scholars have combined the information of GPS, high-precision map, and LiDAR (Light Detection And Ranging) [20] to detect the lane of road surface.