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  As an advanced wireless communication technology currently applied in ITS (Intelligent Transportation Systems), C-V2X can not only address the problem of over one million deaths annually from road traffic accidents, but also extend the blind spot detection capabilities in autonomous driving coverage. Its technical standards and application modes are as follows:   I. Technical Advantages: C-V2X can aggregate information collected in collaborative sensing, update maps using precise road structure information, and distribute localized high-definition (HD) maps based on vehicle location. These enhanced advanced services, such as blind spot detection, remote sensing, remote driving, and platooning, all benefit from C-V2X technology. It can improve road capacity, driver safety, and comfort; as shown in Figure 1, these are the advantages that C-V2X technology brings to autonomous driving. Figure 1. Schematic Diagram of C-V2X Technology Integration and Application   II. Standard Mode: Using 3GPP (3rd Generation Partnership Project) 4G (LTE) or 5G (NR) connections for signal transmission and reception, it operates in two complementary transmission modes; The first is direct communication with vehicles, infrastructure, and pedestrians; in this mode, C-V2X operates independently of the cellular network and uses the PC5 interface for communication. The second is cellular network communication. C-V2X utilizes traditional mobile networks, enabling vehicles to receive road and traffic condition information in their area – this mode uses the Uu interface for communication.   III. Application Prospects: With technological evolution and deployment, fatal accidents caused by human error or road conditions, and serious traffic congestion caused by special circumstances or accidents will no longer be a problem. Through vehicle-to-vehicle (V2V) and vehicle-to-pedestrian (V2P) technologies in C-V2X, risks can be detected before they become threats, and through C-V2X vehicle-to-infrastructure (V2I) and vehicle-to-network (V2N) technologies, warnings can be issued before traffic congestion occurs. These technologies are being put into use successively. The collaborative application of C-V2X, intelligent transportation systems, and 5G will help achieve safer roads and more efficient travel.   IV. Technology The integrated low-latency, high-reliability C-V2X technology enables vehicles to communicate with other vehicles (V2V), pedestrians (V2P), roadside infrastructure (V2I), and the network (V2N), regardless of whether a cellular network is used, thereby improving road safety and traffic efficiency. Autonomous vehicles are typically equipped with advanced sensors: cameras, LiDAR, radar, Global Navigation Satellite System (GNSS), and Controller Area Network (CAN). So why is C-V2X technology still needed for intelligent transportation systems? This is because C-V2X can detect potential hazards and road conditions over long distances. Even fully equipped autonomous vehicles cannot detect non-line-of-sight (NLOS) objects. C-V2X can overcome the NLOS problem by using PC5 interface sidelink communication or cellular networks to provide additional safety features. Vehicle sensors provide the basic functions of autonomous driving; this will not change in the future and is crucial for safety. However, the automotive industry has realized that connectivity is essential for further improving the safety and comfort of L3 (Level 1: Conditional Automation) or L4 (Level 2: High Automation) driving; to achieve higher levels of autonomous driving, vehicles must be interconnected through C-V2X technology.

  C-V2X (Cellular Vehicle-to-Everything) is an advanced wireless communication technology currently used in ITS (Intelligent Transportation Systems) for autonomous driving; this technology extends the coverage of autonomous driving and improves blind spot detection capabilities.   I. C-V2X Technology Characteristics: Compared to commonly used traditional sensors, C-V2X is more cost-effective and more suitable for large-scale deployment. Based on the PC5 interface, C-V2X uses Sidelink technology (direct vehicle-to-vehicle communication) to achieve low-latency UrLLC (critical mission) sensor connectivity, with a communication range exceeding that of conventional wireless networks.   II.C-V2X and Autonomous Driving: In 2020, 5G (NR) technology was fully commercialized globally; mobile communication operators and relevant departments are eagerly anticipating its greater role in people's daily lives due to its low latency, high reliability, and high throughput. Level 3 (conditional automation) or Level 4 (highly automated) autonomous driving is a typical example of 5G (NR) applications, where the URLLC (ultra-reliable low-latency communication) used perfectly showcases the capabilities of mobile technology. The evolution of C-V2X and the deployment of 5G (NR) complement each other, jointly building a new ecosystem that will change the way people drive and manage traffic in the future.   III.C-V2X Applications: Given that approximately 1 million people die in road traffic accidents worldwide every year, making traffic accidents the eighth leading cause of death globally, C-V2X (Cellular Vehicle-to-Everything) is becoming a popular solution to this problem. As a complete communication system, it specifically includes four categories of applications:   V2V (Vehicle-to-Vehicle): Communication between vehicles, such as maintaining safe distance, speed, and lane changes. V2I (Vehicle-to-Infrastructure): Communication between vehicles and road infrastructure, such as road signs, traffic lights, and toll booths. V2P (Vehicle-to-Pedestrian): Communication between vehicles and pedestrians, such as sensing nearby pedestrians or cyclists. V2N (Vehicle-to-Network): Communication between vehicles and the network, such as obtaining infotainment information via the internet and sending vehicle performance data to the car manufacturer.

  I. Core Network Assistance Information in 5G: This is designed to assist RAN in optimizing User Equipment (UE) state transition control and RAN paging strategies in the RRC Inactive state. Core network assistance information includes the information set "Core Network Assisted RAN Parameter Tuning," which helps the RAN optimize UE RRC state transitions and CM state transition decisions. It also includes the information set "Core Network Assisted RAN Paging Information," which helps the RAN develop optimized paging strategies when RAN paging is triggered.   II. Core Network Assisted RAN Parameter Tuning helps the RAN minimize UE state transitions and achieve optimal network behavior. The current specifications do not define how the RAN uses core network assistance information.   Core network assisted RAN parameter tuning can be tuned by the AMF for each UE based on collected UE behavior statistics, expected UE behavior, and/or other available information about the UE (e.g., subscribed DNN, SUPI range, or other information). If the AMF maintains expected UE behavior parameters, network configuration parameters (as described in TS 23.502 [3] sections 4.15.6.3 or 4.15.6.3a), or SMF-derived core network assisted RAN parameter tuning, the AMF can use this information to select core network assisted RAN parameter values. If the AMF can derive the UE's mobility pattern (as described in section 5.3.4.2), the AMF can consider mobility pattern information when selecting core network assisted RAN parameter values. The SMF uses SMF-associated parameters (e.g., UE's expected behavior parameters or network configuration parameters) to derive SMF-derived CN assisted RAN parameter tuning. The SMF sends the SMF-derived CN assisted RAN parameter tuning to the AMF during the PDU session establishment process. If the SMF-associated parameters change, the PDU session modification procedure is applied. The AMF stores the SMF-derived CN assisted RAN parameter tuning in the PDU session level context. The AMF uses the SMF-derived CN assisted RAN parameter tuning to determine the PDU session level "expected UE activity behavior" parameter set, which may be associated with the DU session ID, as described below. Expected UE behavior parameters or network configuration parameters can be provided to the AMF or SMF by an external party via the NEF, as described in Section 5.20.   III. CN-assisted RAN parameter tuning provides the RAN with methods to understand UE behavior, specifically including the following aspects: "Expected UE activity behavior," which refers to the expected pattern of UE switching between CM-CONNECTED and CM-IDLE states, or the duration of the CM-CONNECTED state. This can be obtained from sources such as statistical information, expected UE behavior, or user information. The AMF derives one or more sets of "expected UE activity behavior" parameters for the UE as follows: The AMF can derive and provide the RAN with a UE-level set of "expected UE activity behavior" parameters, which considers the expected UE behavior parameters or network configuration parameters received from the UDM (see Sections 4.15.6.3 or 4.15.6.3a of TS 23.502 [3]) and the SMF for CN-assisted RAN parameter tuning. Control plane CIoT 5GS optimization is used to tune parameters related to PDU sessions. This set of "expected UE activity behavior" parameters is valid for the UE; and The AMF can provide the RAN with a PDU session-level set of "expected UE activity behavior" parameters, for example, considering CN-assisted RAN parameter tuning derived from the SMF, for each established PDU session.   IV. The PDU session-level "expected UE activity behavior" parameter set is associated with and valid for the PDU session ID. The RAN can consider the PDU session-level "expected UE activity behavior" parameters when the user plane resources of the PDU session are activated; "Expected handover behavior," which refers to the expected interval between inter-RAN handovers. This can be derived by the AMF, for example, from mobility pattern information; "Expected UE mobility," which indicates whether the UE is expected to be stationary or mobile. For example, this information can be obtained from the following sources: statistical information, expected UE behavior parameters, or subscription information; Expected UE mobility trajectory, for example, can be obtained from statistical information, expected UE behavior parameters, or subscription information; or UE differentiation information includes expected UE behavior parameters, but does not include the expected UE mobility trajectory (see clause 4.15.6.3 of TS 23.502 [3]), to support Uu operation optimization for NB-IoT UE differentiation (if the RAT type is NB-IoT).   ----The AMF decides when to send this information as "expected UE activity behavior" to the RAN via an N2 request, through the N2 interface (see TS 38.413 [34]). ----The CN assisted information calculation, i.e., the algorithm used and relevant criteria, and the decision on when it is considered appropriate and stable to send it to the RAN, are vendor-specific.