###### Abstract
Safety pilot model deployment (SPMD) is a database using roadside equipment and vehicle-mounted radar and vision to demonstrate the technology of connected vehicles in various environments and analyze driver acceptance of vehicle safety systems [35]. The High D dataset has collected driving data from more than 110,500 vehicles on highways in Germany and is being used in various ADAS and ADS studies [21,38]. Recently, using these large-scale driving databases, research on drivers' driving characteristics and their application to car-following models is being actively conducted [29,39,40,41,42]. In [39], [PERSON] defined the leading vehicle from the driving database and derived parameters of the Gipps model that follows it using artificial neural networks (ANNs). In [42], [PERSON] deduced drivers' characteristics through analysis of the driving database and applied them to the Newell model to analyze driving risk [42]. In [40], based on the NDS, [PERSON] derived the longitudinal acceleration and yaw angle of the following vehicle using reinforcement learning and a fuzzy algorithm. These methods are suitable for analyzing the traffic system by simulating the driving of the vehicle in a simulation environment like one facing an actual human driving. However, since it is difficult to design models for implementing malicious scenarios and control them, the traffic simulation-based method has limitations in ADAS and ADS verification. Therefore, to overcome the limitations of the analyzed ADAS and ADS verification scenario design methods studied thus far, we propose a longitudinal motion scenario design method. In this method, the longitudinal driving characteristics of human drivers were reflected in the surrounding vehicles of the ego vehicle for more realistic scenario design in a simulation environment. First, a simplified vehicle dynamic model expresses the dynamic behavior of the vehicle, and a driver model imitates human driving by using model predictive control (MPC). With the vehicle and the driver model, the test vehicle can adapt to the surrounding environment and drive realistically, and various scenarios can be actively produced. Especially, to improve the consistency of the driver model, we determined the acceleration/deceleration standards for typical human drivers derived from a large-scale driving database analysis. Lastly, the proposed method was verified using computer simulations to confirm human driver imitations and their significance. The computer simulations assumed a driving situation on a straight-line road with a high road frictional coefficient. As the driving characteristics of human drivers vary depending on the driving situation, it was limited to the longitudinal acceleration/deceleration situation. The acceleration/deceleration and jerk, which are the longitudinal driving characteristics of human drivers, are derived in this situation. The characteristics of the sailing/braking start point are derived in this situation as well. To verify this and confirm consistency, the velocity, acceleration/deceleration, and jerk of the proposed method were compared with the acceleration/deceleration section of the randomly selected driver data. This study confirmed the possibility of simulating various drivers by changing the weights and constraints setting of the driver model. Through this, it is possible to create various longitudinal driving scenarios that overcome the shortcoming of the existing scenario design methods. In addition, if the constraints of the proposed driver model are set close to the limit situation, such as close distance to the surrounding vehicle or sudden stop, malicious scenarios can be easily designed with simple parameter changes. This paper is organized as follows. Section 2 introduces a simplified vehicle model in more detail. Section 3 describes the error dynamics between the ego vehicle and surrounding vehicles and the design of an MPC-based driver model. Section 4 explains human driving characteristics derived from a large-scale database analysis.