## 1 Introduction
### Related Works  
Our proposed system utilizes in-vehicle data, aligning with various studies that have explored the integration of machine learning (ML) applications with such data. For instance, research [17; 18] employed SVM and neural network algorithms to identify unsafe driving behavior using in-vehicle sensor data such as vehicle speed, engine speed, and brake pedal pressure, achieving over 90% accuracy with both classifiers. Another study [19; 20] developed a model to identify dangerous driving events using random forests and recurrent neural networks, analyzing data such as acceleration and engine RPM. This model categorized driving behavior into normal, moderate, and aggressive levels. A different approach [21] involved the use of k-means clustering and support vector machines to classify drivers' styles, focusing on vehicle speed and throttle opening. Near-crash prediction has also been a focus, with one study [22] utilizing vehicle kinematics data in multiple ML algorithms, including KNN and AdaBoost, for prediction. Another study [23] proposed a model to identify driver trips using historical data, employing decision trees with an accuracy range of 75% to 100%. Road condition analysis has also been a significant area of research. Researchers proposed a model [24] that detects road potholes using vehicle accelerometers and GPS data, evaluating it using k-means clustering and random forests. Computer vision experts suggested a pothole detection technique using stereo vision cameras and deep learning [25], while a comprehensive review [26] summarized various machine learning approaches for pothole detection. Researchers have also surveyed [27] low-cost road traffic detection techniques using in-vehicle sensors, exploring the use of various sensors such as infrared, accelerometers, and radars. Additional applications of in-vehicle data with ML include vehicle theft prevention [28; 29], driver drowsiness prediction based on cabin air quality [30], and traffic signal detection from vehicle speed profiles [31]. Table 1 shows related works. Figure 1: Categories of connected vehicles. The authors in [20] discussed the advancement of smart city infrastructures via the adoption of machine-to-machine (M2M) communication models. This approach improved system automation by promoting seamless integration between physical and virtual elements in urban environments. They highlighted the development of in-vehicle technologies by automobile manufacturers, aimed at improving traffic management and information dissemination. By integrating vehicles, technologies, sensing devices, and surveillance cameras, the authors propose leveraging these elements to foster intelligent transportation systems (ITS). This, however, introduced new challenges, such as data latency and throughput requirements for certain smart city applications. The proposed model aimed to enhance M2M nodes by creating an interconnected system of electronic devices, communications, and sensors, serving as a bridge between smart cities and vehicles, with the potential for future expansion to various transportation modes. Ref. [32] explored the emergence of smart cities (SC) and urban service robots (USR), underscoring their growing significance in daily life. The SC framework relied on collecting information via a network of sensors, culminating in centralized data hubs to optimize city operations and improve citizen services.