###### Abstract  
This paper proposes a test scenario design method that reflects the longitudinal characteristics of reality for effective verification of advanced driver assistance systems (ADAS) and autonomous driving systems (ADS). Since the target systems interact with the external environment differently from the existing vehicle control system, realistic and various verification scenarios are required for verification. The proposed method consists of a vehicle model for simulating the vehicle behavior and a driver model to actively respond to the driving environment. In particular, the driver model used a model predictive control (MPC) algorithm to reflect the characteristic of human drivers. The longitudinal driving characteristics of human drivers were derived through a large-scale driving database analysis and considered in the driver model. The proposed method was compared with an existing car-following model using computer simulations. It was confirmed that its longitudinal driving behavior is similar to that of human drivers and that various scenarios can be designed by changing the model parameters. advanced driver assistance systems (ADAS); autonomous driving systems (ADS); model predictive control (MPC); scenario design; driver model; naturalistic driving database +
Footnote †: journal: Applied Sciences  
Accepted: 12 December 2022  
Footnote †: journal: Applied Sciences  
be used for designing various scenarios, which defines factors to be evaluated based on a large-scale database [15,20]. Many projects have suggested test scenarios using actual driving/accident data [15,16,17,18]. However, ADAS and ADS designed with known scenarios inevitably have lower reliability for scenarios that have not occurred ever. A malicious scenario-based method is one that defines scenarios near the limit of the mathematical models of the ego and surrounding vehicles [19,20,21]. This method can be verified in a short time because the verification is performed in the unstable regions of the ego and surrounding vehicles. However, when the system is very complex or designing a model of the system is not possible, it is difficult to design malicious scenarios. Last but not least, a traffic simulation-based method can be used to validate ADAS and ADS in a comprehensive environment. A traffic simulation is widely known as a method for macroscopic analysis of traffic volumes for road or signal system design [12,22,23]. This method is based on car-following models to simulate the traffic flow, and its application has recently been increasing for the verification of ADAS and ADS. Table 1 represents the list of widely known car-following models [24,25,26,27,28,29]. Each car-following model commonly uses the time constant \(\tau\) to express the driver's delay. In Table 1, \(v_{f},a_{f},~{}R,~{}\dot{R},~{}R_{min}\), and \(V_{d}\) represent the speed and acceleration of the ego vehicle, relative distance, speed and minimum relative distance with a leading vehicle, and the maximum speed of the ego vehicle, respectively. Other parameters (\(C,~{}C_{v},~{}C_{t},~{}C_{t},~{}A,~{}B,~{}\dot{B}\)) are configured to express various relative distances and speeds with a leading vehicle. Traffic simulation software is designed by improving the car-following models described in Table 1[12,22,23]. Among the car-following models, only the Gipps model considers both acceleration and deceleration, which are the basic parameters governing the motion of the vehicle [28]. Therefore, it is not appropriate to use the remaining car-following models in the verification of ADAS and ADS, where the behavior of surrounding vehicles is important. As studies of ADAS and ADS became active in the 2000s, a large-scale naturalistic database was built to design the behavior of surrounding vehicles [18,30,31,32,33,34,35,36,37]. The 100-car naturalistic driving study (NDS) is a database of 3 million km of driving data from 241 drivers led by Virginia Tech [30].