## 2 Related Work
The result showed that the proposed framework was able to detect distractions in driver behavior up to 97%, thereby reducing road accidents. The proposed framework will detect only behavior indicating that drivers are distracted, but apart from distractions, many factors cause road accidents. The proposed framework also does not provide any recommendations to the driver. In [34], the authors proposed a novel framework called \"driver model adaptation\" (DMA). A transfer learning-based approach was developed to find the data of various drivers and to adapt the obtained data to the dataset of a target driver. The name of this approach is dynamic time warping with local procures analysis (DTW-LPA). Once the adaptation is completed, Gaussian mixture regression is used to train the target driver's model. The proposed model was validated under various experiments with the data collected. The result showed that the proposed framework performs well with more accuracy. It does not detect the behavior of each driver; it uses only the data of other drivers to train the target driver model. It does not provide any real-time recommendations to the drivers based on their behavior. An assistant system was proposed in [35] that uses a bidirectional long short term memory (Bi-LSTM) network to detect the turning behavior of a vehicle with derived parameters such as lateral velocity, lateral acceleration, and heading angle and predicted parameters such as lateral position, longitudinal position, speed, and acceleration of the vehicle, which are predicted by using an online auto regressive integrated moving average (ARIMA) algorithm. The next generation simulation (NGSIM) was used to validate the proposed system, and the result showed that turning behavior was predicted at an accuracy of 94% before initializing turning. However, the assistant system predicts only the turning behavior of a driver, which will not reduce road accidents caused by other situations. In [36], the authors proposed a driver behavior model based on the whale optimization algorithm--restricted Boltzmann machine (WOA-RBM) method. A restricted Boltzmann machine used in deep learning is used to mimic the behavior of a driver. The optimal parameters for the RBM are determined by the whale optimization algorithm. Finally, the output of the proposed method is used to control a vehicle by applying acceleration and deceleration. The proposed driving model was tested and validated in MATLAB, and the results showed that it performed better in prediction and had an accuracy of 90%. Nevertheless, it assists only in acceleration and deceleration and does not provide any assistance for improper handling of a vehicle. It also does not give any recommendations to the driver based on their behavior. A driving safety field (DSF) model was proposed in [37] to predict pedestrians and to avoid the collision of vehicles and pedestrians. This model consisted of five blocks. The first block is an input block that gathers information on pedestrians, vehicles, and the road environment. The second block is for pedestrian trajectory prediction, in which a dynamic Bayesian network (DBN) is used to predict the intention of pedestrians. The third block is for vehicle trajectory prediction, in which the vehicle is assumed to travel at a constant velocity and invariant yaw angle. The fourth block is used to assess risk, and the fifth block provides a pedestrian risk value. A Monte-Carlo experiment was used to simulate the vehicle driving process, and the result showed that the proposed model could be implemented in autonomous vehicles to make decisions for safe driving. However, it does not predict the risk of collision caused by adjacent vehicles, which will also lead to accidents, and it does not provide any recommendations to the driver. The authors in [38] proposed an adaptive cruise control method for analyzing the behavior of adjacent vehicles while lane changing to avoid collisions. First, parameters such as the acceleration and velocity of an adjacent vehicle are estimated.