## 1 Introduction  
The sector of transportation and road safety has transformed in recent years due to the integration of sensor technologies into cars. Advanced smartphone sensors and On-Board Diagnostics (OBD) systems [1] are examples of in-vehicle sensors that have enabled researchers and the automobile industry to collect vast amounts of data about traffic patterns, road conditions [2], and driver behavior [3]. With this abundance of data, predictive models can be created to analyze driving habits, evaluate traffic volumes, and provide up-to-date information on road infrastructure. In-vehicle sensor data can be collected from many sources. The Engine Control Unit (ECU) [4] provides data on engine speed, RPM, fuel consumption, and other engine parameters that can indicate aggressive or fuel-efficient driving styles. OBD offers a standardized interface to access various vehicle data, including speed, acceleration, and braking patterns, which are crucial for assessing driving style. Wheel speed sensors track the rotational speed of each wheel, allowing for the detection of wheel slip and potentially hazardous situations. AI algorithms, in conjunction with in-vehicle data, including OBD and smartphone data, have been used in various fields. For instance, [PERSON] et al. [5] presented a scalable vehicle CO\({}_{2}\) emission prediction model utilizing vehicle OBD-II port data in emission prediction. The proposed method employs a Long Short-Term Memory (LSTM) model based on a Recurrent Neural Network (RNN) to estimate the vehicle's CO\({}_{2}\) emissions from real-time in-vehicle sensor data. Based on OBD data and machine learning, [PERSON] et al. [6] offered an estimation of pollutant emissions in real driving situations. OBD data are also used in predicting a vehicle's trajectory. For instance, [PERSON] et al. [7] presented a technique that allows a vehicle's trajectory to be accurately and individually predicted based on a limited number of inter-vehicle communication signals, such as the vehicle's acceleration and speed, determined by an unsupervised clustering approach. The integration of GPS and OBD data was suggested by [PERSON] et al. [8] as a feasible solution for large-scale car trajectory collection. OBD data are commonly used in fuel economy predictions. [PERSON] et al. [9] proposed a model to improve fuel consumption monitoring databases based on mobile phone terminals and OBD installed in taxis. [PERSON] et al. [10] proposed an approach for fuel consumption forecasting based on OBD data using a multivariate time series method. In-vehicle sensor data can be utilized in vehicle health estimation and vehicle preventive maintenance prediction as proposed in [11; 12]. Several machine learning techniques, such as decision trees and random forests, can be used to predict road conditions and driving styles from in-vehicle data [13; 14]. However, among traditional machine learning algorithms, Support Vector Machines (SVM) are recognized for their high accuracy in classification tasks [15]. SVM identifies the optimal hyperplane that separates data points of different classes by maximizing the margin between the support vectors of these classes. Additionally, SVM employs kernel functions to project the data into a higher-dimensional space, enabling the creation of an effective decision boundary. Therefore, SVM is used as a reference algorithm to provide a baseline for result comparison with our proposed deep learning algorithms. Cameras and LiDAR are advanced sensors that offer visual and spatial data of the surrounding environment, enabling the detection of vehicles, pedestrians, and other road users, which is essential for understanding traffic flow and potential congestion [16; 17].