# A Study on the Evaluation Method of Highway Driving Assist System Using Monocular Camera
## 1 Introduction  
As one of the defining technologies of the Fourth Industrial Revolution, autonomous driving enables a vehicle to recognize its driving environment and generate a path and strategy to drive on its own. SAE International [1] classifies autonomous driving technology into six levels, from 0 to 5, according to the intervention level of the driver and vehicle systems. Currently, level 2 has reached the commercial stage in the form of Advanced Driver Assistance System (ADAS), which include various functions and systems, namely, Adaptive Cruise Control (ACC), Lane Keeping Assist System (LKAS), forward collision warning system, Autonomous Emergency Braking (AEB), active blind spot detection, and Highway Driving Assist (HDA). The safety evaluation of ADAS requires essential criteria of test scenarios, which are made up of numerous situations that can occur while driving. Based on results in Scenario Model Predictive Control, [PERSON] et al. [2] proposed a novel design of control algorithms for lane change assistance and autonomous driving on highways, using scenarios that can be generated by any model- or data-based approach. The experimental results demonstrated the performance of their algorithm in highway lane change situations. [PERSON] et al. [3] proposed traffic scenarios where characteristics of driving behavior were learned by Hidden Markov Models. In addition, they built a knowledge base to specify model adaptation strategies and store priori probabilities according to the scenario characteristics to predict the future behavior of the target car. [PERSON] et al. [4] proposed safety evaluation scenarios based on existing ADAS regulation, which were evaluated via both a simulation and an experimental vehicle test. [PERSON] et al. [5] proposed a test evaluation scenario of an ACC and AEB integrated system, and verified itusing the PRESCAN simulator. [PERSON] et al. [6] proposed scenarios and evaluation items for autonomous driving safety evaluation, which were verified by analyzing the driving data acquired through actual vehicle driving. Additionally, safety evaluation factors were developed based on ISO requirements, previous works, and current traffic regulations at the time. [PERSON] et al. [7] proposed scenarios for the safety evaluation on take-over on highways, wherein they first developed a highway driving scenario, before developing six control transition scenarios. In such studies, based on a proposed scenario, the safety of the system is evaluated through an actual vehicle test or a simulator such as PRESCAN [8; 9; 10] or CarSim [11; 12; 13]. [PERSON] and [PERSON] [14] proposed scenarios and equations to evaluate the safety and functioning of adaptive cruise control systems and verified their effectiveness through vehicle tests. [PERSON] and [PERSON] [15] proposed test scenarios and evaluation equations to assess the safety and functioning of lane keeping assist systems and analyzed the characteristics of each scenario by applying real road conditions representing the driving environment of the Republic of Korea. [PERSON] and [PERSON] [16] designed scenarios for safety evaluation, testing, and verification of autonomous emergency braking systems by comparing theoretical values with the corresponding experimental measurements. [PERSON] et al. [17] devised scenarios to evaluate the safety and functioning of HDA systems, performed simulations using the PRESCAN simulator, and verified the simulation results with real vehicle tests. [PERSON] and [PERSON] [18] developed a learning method that considers the dynamic characteristics of individual vehicles and driver systems, before and during lane changes, and verified the validity of the method through real vehicle tests. [PERSON] and [PERSON] [19] automatically extracted scenario-related features for the LKAS based on deep learning of sensor data; they compared the performances of a convolutional neural network and a recurrent neural network, from which two classification models could be established. [PERSON] et al.