## 1 Introduction  
Autonomous driving enables a vehicle to operate without the need for human driver intervention [1], by relying on sensors (e.g., camera and light detection and ranging (LiDAR)) to perceive the surrounding driving environment, assess potential risks, and plan the most efficient route. The Society of Automotive Engineers (SAE) proposed six levels of autonomous driving, ranging from Level 0 (no automation) to Level 5 (full automation), to describe the capabilities of vehicles on the road [2]. These levels are determined based on four criteria: (1) the responsibility for primary control, (2) responsibility for monitoring the driving environment, (3) responsibility for fallback to a safe state, and (4) driving mode. According to these criteria, current prevailing autonomous driving technologies in the market typically fall between Level 2 (partial automation) and Level 3 (conditional automation) [3], which can be realized through technologies related to the autonomous vehicle itself (i.e., standalone autonomous driving) [4]. One of the ways to achieve Level 4 (high automation) and Level 5 (full automation) is cooperative autonomous driving [5], which consists of advanced infrastructure and communication technology. The advanced infrastructure, named intelligent transportation systems (ITS), consists of several components such as road traffic safety facilities [6], vehicle detection systems (VDS) [7], roadside units (RSU) [8], local dynamic map (LDM)systems [9], and traffic control centers [10]. The communication technology, called vehicle-to-everything (V2X) communication, can be realized via ultra-low latency communication speeds with other vehicles and infrastructure over wired and wireless networks. Additionally, cybersecurity must be ensured, including the blocking of eavesdropping, prevention of message forgery, blocking of malicious control message transmission, and protection against unauthorized information acquisition [11]. Currently, countries, automakers, and other stakeholders are working toward advancing autonomous driving technology from a policy, technology, and standards perspective. At the national policy level, the United States (U.S.) [12] has provided direction on a wide range of areas, including advanced manufacturing, artificial intelligence, related infrastructure, regulations, taxation, intellectual property rights, and the environment required to promote AV technology through \"Automated Vehicle 4.0.\" China [13] has designated six regions, including Beijing, Shanghai, and Guangzhou, as pilot cities for the development of connected cars and smart city processes, and is actively developing the relevant technologies. Europe [14] has prepared a joint roadmap, \"Automated Driving Roadmap\", centered on the European Road Transport Research Advisory Council, proposing fully autonomous driving by 2030. South Korea [15] is investing private and public funds to lead the autonomous driving market by 2030 through Korea's national strategy for autonomous vehicles. In addition, technologies that focus on different strategies for traditional and new automakers are being developed. To gain the competitive advantage of autonomous driving technology, existing automakers are acquiring startups or establishing joint ventures and collaborating to conduct autonomous driving research with ventures. For example, Volkswagen and Ford [16] acquired the autonomous driving platform company \"Argo AI\" and established a joint venture as part of a strategic alliance to jointly develop autonomous driving technology.