# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Cognitive Models of Lane Change
#### 2.3.3 Information-Processing Model  
The information-processing model is a classic cognitive model to describe the decision process of a lane change. The model divides lane changing into three stages, namely perception, judgment, and execution. In the first perception stage, the driver acquires information about the road environment mainly through hearing and seeing. In the second judgment stage, when the driver perceives the environmental information of the relevant road, they analyze and judge it according to their personal driving experience, to decide whether to change lanes. In the third execution stage, when the driver decides to change lanes, they will control the speed and operate the steering wheel of the vehicle to perform the actual lane change behavior. Lane change behavior has received more and more attention, and more and more algorithms have been developed. However, most of the algorithm models are derived and verified using the vehicle trajectory data, without considering the driver's behavioral characteristics. [PERSON] et al. [128] studied relevant information concerning drivers \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Input Variables** & **Algorithm** & **Key Issues to Be Resolved** \\ \hline \multirow{4}{*}{[124]} & \multirow{4}{*}{\begin{tabular}{c} Electrocardiogram (ECG), g-alvanic skin \\ response (GSR), and respiration rate (RR) \\ \end{tabular} } & \multirow{4}{*}{MTS-GCNN} & This algorithm uses economical \\  & & & physiological sensors instead of \\  & & & expensive ones to predict the driver’s \\  & & & lane change behavior accurately \\  & & & and effectively \\ \hline \multirow{4}{*}{[125]} & \multirow{4}{*}{\begin{tabular}{c} 47 drivers participated in the experiment \\ and 141 valid lane change records \\ were collected \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Fuzzy C-means clustering \\ algorithm + FCMNN \\ \end{tabular} } & The intention recognition of driving \\  & & & behavior is deeply analyzed to \\  & & & improve the safety of lane \\  & & & change behavior \\ \hline \multirow{4}{*}{[126]} & \multirow{4}{*}{\
\begin{tabular}{c} CranData \\ \end{tabular} } & \multirow{4}{*}{CNN + RNN + LSTM} & The two-stage learning framework \\  & & & can significantly improve the \\  & & & flexibility and accuracy of the model \\ \hline \multirow{4}{*}{[127]} & \multirow{4}{*}{
\begin{tabular}{c} Longitudinal velocity, lateral velocity, \\ longitudinal acceleration, lateral \\ acceleration, vehicle heading, and \\ deflection angle \\ \end{tabular} } & \multirow{4}{*}{TCN-ATM + LC-IR-SP} & The model has great potential to \\  & & improve the perception and \\ \cline{1-1}  & & prediction capabilities of autonomous \\ \cline{1-1}  & & vehicles and improve vehicle \\ \cline{1-1}  & & control strategies \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Input parameters of different artificial neural network lane-changing models and key problems to be solved. Figure 6: Modeling framework for lane change intention recognition and driving state prediction [127]. and classified them according to their background information and verbal description, to combine the drivers' lane change behavior and driving characteristics into the lane change model. Both types of information contained quantitative and qualitative responses and were incorporated into the lane change model. The driving behavior of the surrounding vehicles is an important factor in driving safety. [PERSON] et al. [129] comprehensively considered the relationship between the two factors and proposed a prediction method based on a fuzzy reasoning system (FIS) and long short-term memory (LSTM) neural network. The fuzzy reasoning system is used to simulate the driver's cognitive process in the driving environment, and the traffic environment information is transformed into the feasibility of lane change.