## 1 Introduction  
Methods for solving environmental problems caused by vehicles have received significant attention in recent years, and one typical approach is eco-driving. Eco-driving is an economic, environmentally friendly method that reduces air pollution and saves fuel consumption. It is a driving strategy that aims to reduce unnecessary acceleration and deceleration, thereby spending less time and fuel to travel the same distance. Eco-driving is one of the main goals of the intelligent transport systems (ITSs) along with road safety, comfort, and efficiency [1]. Recent advances in information and communication technology (ICT) have enabled high-speed communication between components of the transportation system, and research on a cooperative intelligent transport system (C-ITS) based on the connectivity between the components has received significant attention. A cooperative eco-diving system is one of the representative systems of a C-ITS that has improved the performance of eco-driving systems by utilizing connectivity technology. Many engineers have been developing cooperative eco-driving algorithms, and verification of such algorithms is needed before deploying cooperative eco-driving applications. [PERSON] et al. [2] proposed an optimal controller for eco-driving of hybrid electric vehicles (HEVs) by using present and future information obtained through ITS, such as road topography and dynamic speed limit on a rolling terrain. [PERSON] et al. [3] proposed a centralized traffic management solution that predicts future traffic flows and balances the traffic flows using all the traffic data in a city, obtained through connectivity technology. The solution was validated in large-scale scenarios and the experimental results showed that the solution improved the travel times and average travel speed of vehicles. Simulations have been used as a technique to develop and evaluate systems and offer a cost-efficient and safe environment for development and evaluation. In the context of C-ITS testing, typically used simulation tools include network simulators, traffic simulators, and driving simulators. [PERSON] et al. [4] proposed a simulation-based toolchain to identify and verify critical scenarios for connected and automated vehicles by integrating traffic, vehicle dynamics, and network simulation. They identified the critical scenarios to be tested before market penetration of the connected and automated vehicle (CAV) system by considering both functional and nonfunctional requirements, and verified the concrete scenarios using the simulation tool. Likewise, integrations of different simulators are commonly used in the C-ITS industry; the typical approach is the use of integration of traffic networks and network simulators, i.e., a so-called vehicular ad hoc network (VANET) simulator [5; 6; 7; 8; 9]. Although human factors are important to evaluate the performance of C-ITS, only a few studies have utilized a driving simulator involving human drivers [10; 11; 12; 13]. Driving simulators are effective tools for developing vehicle systems and studying human factors, which are normally missing when using VANET simulators. In addition, driving simulators support the development of specialized electronic control units (ECUs) under realistic conditions using a hardware-in-the-loop (HIL) simulation. HIL simulation enables the specialized ECUs to be tested by providing interaction with all the rest of components of a vehicle system [14]. However, with respect to C-ITS testing and evaluation, driving simulators usually do not support realistic modeling of vehicle-to-everything (V2X) communication, which is easily handled by the VANET simulator. Thus, an integration of all three types of simulators is needed to develop and evaluate a service of C-ITS precisely. Therefore, this study presents an integrated driving HIL (IDHIL) simulator consisting of driving, traffic, HIL, and a network simulator for testing and evaluating a C-ITS, particularly a cooperative eco-driving system.