# In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning
## 2 Related Work  
Since our proposed system uses in-vehicle data, this section explores more related work about in-vehicle data and ML applications. [PERSON] et al. [11] used two ML approaches and in-vehicle sensor data to identify unsafe driving behavior by the driver. They used SVM and neural network algorithms for classification. The input features to the ML system were the vehicle speed, engine speed, engine load, throttle position, steering wheel angle and Brake pedal pressure. Classification results of this study showed an average accuracy above 90% for both classifiers.  
[PERSON] et al. [12]. proposed a model for dangerous driving events using in-vehicle data. Random forests and Recurrent Neural Network were used to classify the data. The authors used features such as acceleration, brake pedal position, acceleration pedal position, engine RPM and torque. The danger level classified as normal, moderate and aggressive. [PERSON] et al. [13] proposed k-means clustering-based support vector machine (kMC-SVM) method to classify drivers into two types: aggressive and moderate. Vehicle speed and throttle opening were treated as the feature parameters to reflect the driving styles.  
[PERSON] et al. [14] introduced a machine learning model for near-crash prediction from observed vehicle kinematics data. Vehicle kinematics data, such as speed, longitudinal acceleration, lateral acceleration, yaw rate and pedal position, were used as input features for multiple ML systems. The authors utilized several machine learning algorithms, such as K nearest neighbor (KNN), random forests, support vector machine (SVM) and adaptive boost (AdaBoost), to predict near-crash situations. The AdaBoost algorithm showed a better recall and F-score than other algorithms. A system which can identify the driver trip using historical trip-based data collected from in-vehicle data was proposed by [PERSON] et al. [15]. Decision trees obtained an accuracy between 75% and 100%.  
[PERSON] et al. [16] proposed a model to detect road potholes using vehicle accelerator information and GPS data. The k-means clustering algorithm was applied on the training data to build the model. Random forests classifier was used to evaluate this model on the test data for better prediction. [PERSON] et al. [17] proposed a computer vision approach to detect potholes using stereo vision camera and deep learning algorithm. [PERSON] et al. [18] provided a review for potholes techniques using machine learning. The paper summarised the different approaches for potholes detection using vibration sensors, accelerometer, 3D construction and 2D images.  
[PERSON] et al. [19] provided a survey for low-cost techniques to detect road traffic using in-vehicle sensors. The techniques include applications of infrared and visible light sensors, wireless transmission, accelerometers, magnetometers, ultrasonic and microwave radars as well as acoustic sensing.  
There are many other applications that uses in-vehicle data with ML. For example, a vehicle theft prevention and driver identification system was proposed by [PERSON] et al. [20; 21]. A system to predict the driver's drowsiness based on the air quality presented in the cabin car was proposed by [PERSON] et al. [22]. [PERSON] et al. [23] proposed a system to address the problem of detecting traffic signals from a set of vehicle speed profiles.  
The significance of our research is in proposing a low-cost prediction system for road conditions and driving style. In order to reduce the system cost, general CAN were was used as input features to the ML system. No additional cost for special sensors are required by the system. Furthermore, the chosen ML algorithms are inexpensive to implement and they do not require a complex computing system.