## 2 Related Work
After this, the trajectory of the adjacent vehicle is predicted. Risk assessment and risk minimization are carried out by the proposed method. The method was evaluated in a real-time situation, and the performance of each stage of the method was analyzed. The result showed that the method reduces collisions, thereby improving driver safety. However, it does not give any recommendations to the driver based on speed, braking, and control of steering. In [39], the authors proposed a risk prediction method used during lane changes. The physiological information of the driver is obtained from sensors fixed on the driver. The hidden Markov model (HMM) is used to reduce the driving risk with the information obtained from the sensors. Physiological information such as ECG factors and eye movement factors are evaluated before predicting the driver's risk. The dynamic factors of vehicles such as average speed and acceleration are also evaluated to reduce false input data. The proposed method was tested in MATLAB, and the result showed that this method predicted the driver's risk more accurately. The Table 1 above represented the research gaps in the state-of-the-art works in which parameters, Algorithm, and limitations of the existing works are given. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**Objective**} & \multicolumn{4}{c}{**Input Parameters**} & \multirow{2}{*}{**Algorithm/Setup**} & \multirow{2}{*}{**Features Extracted/**Working**} & \multirow{2}{*}{**Limitations**} \\ \cline{3-4} \cline{6-9}  & & **Physiological** & & & & & & & \\ \hline \multirow{3}{*}{[25]} & Behavior analysis & \(\times\) & \(\checkmark\) & \(\times\) & Genetic algorithm-fuzzy-C mean clustering (GA-FCM) & Acceleration & Lack of consideration of adequate \\  & & & & & & & & & Over speed & information results in inefficient behavior \\ \hline \multirow{3}{*}{[26]} & Behavior analysis & \(\times\) & \(\checkmark\) & \(\times\) & \(\times\) & Neuro-fuzzy system with principal component analysis (FIS-PCA) & \multirow{3}{*}{\
\begin{tabular}{c} Feedback provided by this model is very limited, and latency is high as processing is performed in cloud. \\ \end{tabular} } \\  & & & & & & & & \\ \hline \multirow{3}{*}{[27]} & Behavior analysis & \(\checkmark\) & \(\times\) & \(\checkmark\) & Deep convolutional neural network (DCNN) & Heart rate &
\begin{tabular}{c} Feedback provided by this model is very limited, and latency is high as processing is performed in cloud. \\ \end{tabular} \\  & & & & & & & & \\ \hline \multirow{3}{*}{[28]} & Edge offloading & - & - & - & Multi-objective evolutionary solutions to increase resource decomposition (MOEA/D) & Generates optimal solutions to increase resource utilization and reduce delay & Not suitable for large-scale, real-time environment in which increased computation results in overloading. \\ \hline \multirow{3}{*}{[29]} & Behavior analysis & \(\checkmark\) & \(\checkmark\) & \(\times\) & Smartphone mobile application & Forehead color & This assistance gets disturbed due to connectivity problems \\  & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \cline{1-1}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research Gaps in Previous Works.