# Advanced Sensor Integration and AI Architectures for Next-Generation Traffic Navigation
## 2 Materials and Methods
### Azure's Content Safety and Pothole Integration  
Azure's Content Safety is used as an Azure Detection Safety Component (ADSC) service in our route optimization system, and it provides \(RSa_{k}\) by automatically identifying and reporting traffic incidents. The ADSC analyzes images to identify and classify content that may be deemed sensitive or require moderation. This includes detecting violent scenes, accidents, and other distressing events. By leveraging advanced ML algorithms, the service accurately discerns various types of sensitive content, providing actionable insights for real-time incident reporting. The flowchart presented in Figure 2 contains the operational workflow for ADSC and the Pothole Procedure Component (PP). It outlines the process for detecting road hazards, such as potholes, accidents, and violence, using an ESP32-CAM module (manufactured by Espressif Systems Co., Ltd., Shanghai, China) and an MPU-6050 gyroscope (produced by TDK InvenSense, Shenzhen, China). Potholes are identified through the MPU-6050 gyroscope, which measures angular velocity along the Z-axis. When the difference between consecutive readings exceeds a predefined threshold (e.g., 0.8 rad/s), it indicates a road irregularity, and the system logs the event as a pothole in the \"UserTrafficReport\" table, including GPS coordinates and timestamps. The pothole identification system is based on variations between the positions of the device at two consecutive points. A pothole is identified at the corresponding geographical coordinates if the variation exceeds an empirically imposed threshold. Simultaneously, the ESP32_CAM captures images of the detected hazard, which are also considered traffic anomalies. These events are analyzed using the ADSC algorithm to identify additional incidents, such as accidents or traffic violence. ADSC utilizes Azure Cognitive Services to classify sensitive content, including accidents and violent scenes, providing actionable insights for real-time incident reporting and response. Figure 2 presents the PP and ADSC procedures involved in real-time incident reporting to the database. These procedures do not modify the users' trajectory. The values stored in the database with these procedures influence the future computed trajectories. The captured images are transmitted in real time from the ESP32-CAM to the cloud infrastructure using Wi-Fi connectivity. Upon receipt, each image is processed by the ADSC service. The service evaluates the image for indicators of sensitive content, violent incidents, and accidents. The service returns a set of labels and confidence scores indicating the presence and likelihood of various sensitive content types within the image. Simultaneously, the MPU-6050 sensor monitors vehicle motion and detects significant variations that indicate the presence of potholes. These abrupt changes in the gyroscope enable accurate identification and logging of pothole locations, enhancing the system's real-time ability to assess road conditions. Figure 3 illustrates the custom-designed assembly for recording traffic events. It comprises an ESP32-CAM module manufactured by Espressif Systems in Shanghai, China, and an MPU-6050 sensor produced by InvenSense, a subsidiary of TDK, in Tokyo, Japan. These components work in tandem to capture and process real-time traffic incidents. The assembly is algorithmically managed using the Arduino IDE, enabling data acquisition. The incident reporting block identifies an incident with a confidence score exceeding a predefined threshold and is classified as a confirmed event. An entry is automatically created in the Azure SQL Server database within the UserTrafficReport table for each confirmed incident. This entry includes:  
* Latitude and longitude. Precise geographical coordinates where the incident was detected;
* Event type. Classification of the incident, such as \"Pothole\" or \"Incident,\" based on the detected content;
* Timestamp. The exact time when the incident was recorded.