# A Study on the Evaluation Method of Highway Driving Assist System Using Monocular Camera
## 1 Introduction
[20] proposed a recognition algorithm using real-time driving image data and a quantitative recognition rate based on camera images. [PERSON] and [PERSON] [21] proposed a system that can automatically detect moving obstacles using a camera-equipped remote control car, and operated the vehicle to the target point. The results of this study were verified through experiments in the actual driving environment. [PERSON] [22] proposed a motion tracking algorithm for motion recognition of images using the front camera of a vehicle. [PERSON] et al. [23] reduced error occurrence through learning to detect vehicles in wide angle lenses with severe distortion, and verified it through an actual car test. [PERSON] and [PERSON] [24] presented a novel instrument for pedestrian detection by combining stereo vision cameras with a thermal camera. The evaluation results showed that it significantly outperforms the traditional histogram of oriented gradients features. [PERSON] et al. [25] described a perception system for autonomous vehicles, which performs information fusion to recognize road environments. The proposed perception system was validated on various roads and environmental conditions using an autonomous vehicle. [PERSON] and [PERSON] [26] proposed novel methods using computer vision technologies. The experimental results showed that the proposed algorithms can correctly identify lanes with high accuracy in real time, and are robust to noise and shadows. [PERSON] and [PERSON] [27] proposed a method for finding and tracking road lanes for vision-guided autonomous vehicle navigation. The new lane detection method was tested on real road images and achieved robust and reliable results. [PERSON] et al. [28] presented a novel object detection and identification method that fuses the complementary information obtained by two types of sensors. [PERSON] [29] used only camera sensors to predict the path of surrounding vehicles and showed high accuracy with deep learning technology, while [PERSON] [30] proposed a method to measure the distance and speed of a moving object by extracting the difference image from a stereo vision system. [PERSON] [31] proposed a single camera-based front collision warning system using deep learning and OBD2, and verified its performance through a collision experiment. [PERSON] and [PERSON] [32] proposed a method to estimate the distance from the preceding vehicle using Hough Transform and Kalman filter in a monocular camera. [PERSON] et al. [33] proposed a measurement method of the distance to the target vehicle using two images, and calculated the distance using the ratio between the sizes of the objects projected on the two images. [PERSON] et al. [34] detected a lead vehicle using a monocular camera, and [PERSON] et al. [35] proposed a vehicle-detection algorithm using camera calibration. The present study proposes an evaluation method of HDA systems using a monocular camera and verifies its performance through actual vehicle testing. Unlike simulation, there are many variables to be considered during real-world testing. Hence, an actual vehicle test is necessary for the safety evaluation of ADAS. In these tests, there is not only a need for expensive equipment, such as Data AcQuisition (DAQ) and Differential Global Positioning System (DGPS), which measure the dynamic information of the vehicle, but also of experts who can handle such equipment. Consequently, there is a significant amount of time and cost involved in conducting actual vehicle tests. To solve these problems, we propose a method that can evaluate the safety of HDA systems using only a monocular camera (commercial black-box camera with 30 frames per second), as cameras are affordable, easy to handle, and have good accessibility. We assume that the lanes, rear tires of the lead vehicle, and vanishing point are detected in the image captured by the monocular camera. In general, the rear tires of the lead vehicle are detected at a safe distance between vehicles on the highway.