The proliferation of autonomous driving technology and services worldwide has rapidly advanced, driving innovation in the established road transportation landscape. In particular, with the rapid increase in the number of autonomous vehicles, various research and pilot projects are underway. However, studying the interaction between autonomous and self-driving vehicles on road networks and evaluating them is constrained by the low Market Penetration Rate (MPR) of autonomous vehicles. It aims to evaluate autonomous cooperative driving services in a simulated environment where autonomous and conventional vehicles coexist on road networks. The study comprises the following procedures: (1) Surveying the current state of research on global autonomous cooperative driving services, (2) Reviewing various micro-traffic simulation tools, selecting an appropriate tool, and implementing autonomous cooperative driving services on a simulated network based on the actual network, (3) Conducting a comprehensive evaluation of mobility and safety aspects based on autonomous cooperative driving service scenarios. This study explores time travel and speed/acceleration-based Surrogate Safety Measures (SSM) indicators to assess detour degrees induced by service applications. Through grouping vehicles based on the same Origin–Destination (OD), the research aims to provide insights into the effectiveness of these measures in quantifying detours and understanding the implications of service applications on traffic flow. The network is updated based on Daejeon city in South Korea to achieve a realistic simulation, and a scenario involving route-change and lane-change services among various autonomous cooperative driving services is selected. As the development and widespread adoption of future autonomous driving technology become more certain, it is expected that this research will facilitate operational and regulatory decision-making in the future.