## 1 Introduction  
Vehicle detection has attracted considerable attention in the field of object detection technology, with an increase in the demand for automotive safety and autonomous vehicles and an increase in the number of countries implementing institutional support for driver safety. Reducing the number of traffic accidents directly affects not only human lives, but also many social costs. At the very center of social change are advanced driver assistance systems (ADASs). Vehicle detection is the most basic and important technology underlying ADASs, and vehicle detection research has considerably developed in recent years. On-road vehicle detection has become a significant research topic in the ADAS context with the development of various available sensing technologies (radar, lidar, camera, etc.) and the emergence of multi-core and graphics processing unit (GPU) computing [1]. Vehicle detection is largely divided into technologies that use vision sensors and those that do not. In contrast to other sensors, vision-based vehicle detection can directly recognize vehicles and is a hot research field because of its price advantage. Vision-based vehicle detection methods can be categorized into appearance- and motion-based methods [2]. Appearance-based methods have been studied extensively for monocular vehicle detection frameworks to recognize the vehicle directly in the image region of interest (ROI). Appearance-based vehicle detection methods extract appearance features from training images to train the classifier using machine learning algorithms and then identify vehicle(s) in the new image ROIs. Many appearance features have been proposed to detect vehicles, including color, symmetry, edges, and histogram of oriented gradients (HOG) features [3]. The following two best practice appearance-based vehicle detection methods have been developed thus far:HOG features are extracted from each training image, and the classifier is trained using the support vector machine (SVM) algorithm [4]. 2. Local binary pattern (LBP) features are extracted from each training image, and the classifier is trained using the adaptive boosting algorithm (AdaBoost) [5]. Motion-based vehicle detection methods generally use optical flow and occupancy grids. Optical flow vehicle detection has been proposed for monocular vehicle detection frameworks [6], and recent dynamic grid processing developments have allowed an efficient use of occupancy grids to monitor highly dynamic scenes; therefore, grids have become a generic tool for obstacle detection [7]. Appearance-based methods generally use monocular vehicle detection, whereas motion-based methods use stereo vehicle detection. The commercial availability of monocular vehicle detection systems means that appearance-based methods have attracted more research and practical application attention than motion-based methods, but they do not provide three-dimensional (3D) depth. Handcrafted features, such as HOG and LBP, have been widely used by appearance-based vehicle detection methods to discriminate among vehicle images. Handcrafted features are also called middle-level features. Although vehicle detection frameworks that use middle-level features achieve moderate performance, the features are insufficient to fully represent characteristic vehicle image information. Therefore, in this paper, we propose a vehicle detection framework using a selective multi-stage feature fusion method selectively extracted from convolutional neural networks (CNNs) to improve vehicle detection performance. CNN is a type of deep learning, mainly used to classify images, where vehicle image features are learned in each layer's feature map. The proposed vehicle detection framework does not use CNN as a classifier, but uses it only to extract specific features in the feature maps of each layer, with the proposed algorithms using visualization techniques. Thus, not only can the characteristic information of the learned features be examined, but the features suitable for vehicle detection can also be selected.