# PC5-Based Cellular-V2X Evolution and Deployment
## 5 PC5-Based C-V2X Deployment in Taiwan  
In this chapter, some Taiwan field tests and commercial projects, whose C-V2X networks are implemented by network solution provider Nokia, are introduced. As a C-V2X technical expert, the author from Nokia provides a sequence of PC5-based C-V2X system integration solutions. (1) Translating traffic light control signaling into C-V2X internal masses recognized by RSU/OBU to realize SPAT application. Normally, autonomous driving vehicles are equipped with cameras and artificial intelligence that can recognize traffic light information. However, recognition accuracy is easily affected by bad weather or occlusions. Our proposed solution is designed to be more robust against any conditions that can hinder visual recognition. (2) Utilizing AI technology, which has shown state-of-the-art performance in many problem domains [24; 25; 26], for VRUCW application. Deep Learning-based Vulnerable Road User Detection and Collision Warning can be provided by PC5-based C-V2X system architecture. (3) Integrating C-V2X to Automated Driving Systems (ADS) for enhanced safety. By this solution, ADS can monitor road conditions, detect potential problems, and take action to avoid road accidents. The success of these projects will lay a solid foundation for the coming 5G NR-V2X. * **Solution1: Traffic Light Control System Integration** To realize SPAT application locally, we have designed a system architecture shown in Figure 6. Accordingly, the PC5-based C-V2X SPAT application has been launched successfully and is introduced in this chapter. 1. We can collect traffic light information from the light controller directly. 2. Light signaling collection program is responsible for receiving roadside light signal information, including light phase, color and remaining time, etc. All this information is sent to RSU. 3. RSU reads and encapsulates them to C-V2X protocol messages. 4. RSU broadcasts C-V2X messages to OBU via PC5 interface. 5. OBU installed in the autonomous driving vehicle analyzes and filters these messages and then sends them to IPC (Industrial PC) of autonomous driving system for slow or stop control. 6. User Interface (UI) displays light information for intuitive C-V2X technology presentation. * **Solution2: C-V2X VRUCW Application System Integration** For the PC5-based C-V2X VRUCW application, we have developed a network diagram shown in Figure 7.  
Figure 5: Timeline from the initial introduction to the mass market deployment of C-V2X Use cases [22]. 1. The VRUCW application can be regarded as P2I2V service ( Pedestrian to Infrastructure to Vehicle). The IP cameras must be built on road areas for both line-of-sight (LOS) and non-line-of-sight (NLOS) monitoring purposes. 2. It uses an AI server equipped with a series of deep learning technologies such as CNN (Convolutional Neural Network) and SSD (Single Shot Detection). Objects will be detected if any pedestrian passes an area under camera coverage. 3. The AI server transfers the analysis result with object recognition and movement prediction to RSU, which broadcasts this information to all the OBUs under the coverage of this RSU. 4. OBU is responsible for combining vehicle information such as speed, heading, and location to determine if a collision is approaching. We use the target classification algorithm to determine the pedestrian's direction for subsequent warning possibility calculation. 5. Supposing that there is a risk of collision between pedestrian and host vehicle, for example, their distance is within 50 meters and driving speed of vehicle is more than 10 km/hour. We use below Algorithm 1 for collision warning trigger. **Definition 1**. : _Head\({}_{h}\): Azimuth angle of the current host vehicle in position point \(H\). Head\({}_{n}\): Azimuth angle of the target pedestrian in position point \(N\). \((-180^{\circ},180^{\circ})\): The range of azimuth angle for Head\({}_{h}\) and Head\({}_{n}\).