# Fusing Appearance and Prior Cues for Road Detection
## 1 Introduction  
Road detection, a popular research topic in computer vision, is a key module for modern autonomous driving systems and driver assistance systems. Furthermore, it can serve as a preprocessing step for challenging tasks such as collision warning, lane keeping, vehicle detection, and pedestrian detection [1; 2; 3]. Road detection refers to detecting drivable road areas ahead of an ego-vehicle by way of assigning each image pixel as belonging or not belonging to the road surface. Despite the attention it has received and the considerable progress made in the past few years, road detection still remains challenging since algorithms must be able to deal with continuously changing backgrounds, varying illumination (i.e., shadows and highlights), and the variability of road appearance (i.e., size, shape, and color). Urban scenarios may present additional challenges due to the presence of lane markings, vehicles, pedestrians, and infrastructure elements. Recently, many algorithms have been proposed to group road pixels by training a classifier offline using images with finely annotated road pixels. [PERSON] et al. [4; 5] trained a support vector machine (SVM) classifier on thousands of annotated images and then used it to classify the extracted features as either road surface or non-road surface. [PERSON], [PERSON] et al. [6] presented a structured random forest based road detection algorithm which can model the contextual information efficiently to classify road pixels. The algorithm outperforms the classical pixel-wise random forest based methods both in accuracy and efficiency. Moreover, driven by the great success of deep learning, vision based road detection methods achieve unprecedented results. [PERSON] et al. [7] proposed the use of a convolutional neural network (CNN) combining a novel online texture descriptor to learn features to recover the scene layout and detect road areas from a single road image. The novelty of the algorithm relies on using CNN, which provides a relative improvement of 7% compared to the baseline. The first end-to-end semantic segmentation model was proposed by [PERSON] et al. [8], known as the fully convolutional network (FCN). After that, significant progress was made in road detection using a large variety of deep learning based methods [9; 10; 11]. Especially in [12], a network-in-network architecture taking advantage of the contextual window was used and converted into a FCN after training. Experiments conducted on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) road detection benchmark demonstrated the effectiveness of this algorithm and showed that it outperforms state-of-the-art methods. In [13], an adapted deconvolution approach was proposed based on VGG (Visual Geometry Group Network) [14] as the encoder and FCN as the decoder for joint classification, detection, and semantic segmentation. However, these classification-based road detection algorithms employing certain machine learning approaches usually necessitate the optimization of a large number of parameters. Especially, the deep learning models often require numerous FLOPS (Floating-point Operations Per Second) and millions of parameters that need to be optimized via back-propagation, relying on the latest graphic processing unit (GPU) for fast computing and the collection of large amounts of finely annotated images, which are often quite complex, expensive, and time-consuming to obtain. Furthermore, these classifiers learned using the training set are ambiguous and may fail to find road pixels of images in the testing set since the road surface may vary significantly in different scenarios. In this paper, to address these limitations we estimate the road model online using appearance cues which show robustness to the highly dynamic nature of road surfaces and reduce the possibility of misclassifying road and non-road regions. Common algorithms using appearance cues for road detection can be coarsely categorized as texture based and color based methods.