## 2 Related Work
A safety distance was computed for giving a warning message to the following vehicles. Furthermore, there were four factors that were used to analyze the crash risk: driver behavior, vehicle motion, road traffic, and weather conditions. However, lane change deviation was not predicted in this approach, and the health parameters of the driver were not extracted, which increases the misclassification rate. Using an advanced driver assistance system for increasing safety was proposed in [26]. Driving security and quality for both passengers and drivers are ensured in this paper. A driver complex state (DCS) is predicted using non-obstructive sensors, and artificial intelligence (AI) is used to uncover the driver state. This paper considers vehicle information, driver data through a smartphone, smart wearables, and external data (i.e., road condition, weather condition, lane occupation, and actions of other vehicles). However, personalized recommendation is not effectively designed in this paper, in which the emotional state of the driver is not predicted. Vehicle moving patterns become a more powerful input parameter that aids driver behavior analysis. An approach for the effective monitoring of drivers current state of action was proposed in [27]. In this paper, driver distraction was predicted by designing driver monitoring systems. The driver's historical travel data was collected based on two aspects: short-distance and long-distance drivers. Then, driving time, average speed, and frequency and length of breaking time were predicted to analyze the driver's behavior. However, this paper does not provide the specific distraction type of the driver, which must be detailed to correlate with the environment and to generate the corresponding warning messages to the driver and to other vehicles in the ITS. Drivers' physiological parameters must be observed through smart wearables to monitor the health issues of the driver. With the lack of information about drivers, the presented study is not able to determine the behaviors of the different profiles/characteristics of the driver. A cloud-based approach for the detection of driver fatigue was proposed in [28]. In this paper, driver fatigue detection was implemented by using multiple sensors and the reports were processed in the cloud environment. Multimodal feature processing was investigated in this paper, such as facial PERCLOS features, mobile sensors, and inbuilt vehicle sensors, were used to analyze the performance of the driver. There were several driver styles that were predicted and studied in this paper using machine learning with rule-based and deep learning-based approaches. For a real-world environment, it is necessary to compute all of the different parameters of the driver in a DL model. A deep learning model based on analyzing of interaction between the user and the road was proposed in [29]. The proposed stacked autoencoder (SDAE) was used to extract the features of driver behavior from a dataset that included real driving test and GPS sensor records. The dataset included high-dimensional raw driver behaviors. Cubic representation was used to extract the 3D features, in which the color trajectories represented the driving path. The RGB color features were extracted for trajectory analysis. Then, a windowing process was applied to normalize the data, which converts the dataset into a number of frames based on the time. In [30], the authors proposed a mobile app for monitoring driver behavior and provided recommendations for preventing accidents. The proposed method used ontology for collecting driver and vehicle information, which helped to provide a recommendation for dangerous states. For this, the mobile phone was used as a front camera, gyroscope, accelerometer, GPS, navigation map, and microphone to collect the information. The main objective of this research was to provide a recommendation to reduce the number of accidents. The proposed system included two processes: dangerous state classification and detection.