# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Cognitive Models of Lane Change
#### 2.3.3 Information-Processing Model
Intelligent driving systems cannot make human-like decisions just by detecting lane change behavior; in fact, poor environmental cognition is the main reason why intelligent driving systems cannot make correct decisions on the road like humans, so identifying specific lane change strategies can provide support for improving safety performance. The research objective of [PERSON] [134] was to identify subjects' different lane change strategies when the rear vehicle in the target lane approached the lane change node. In the experiment, 42 experienced drivers (37 males and 5 females) were selected to participate in the experiment, to obtain different lane change strategies, and the obtained lane change types were divided into three types: forced, concession, and waiting. The forced lane change strategy is shown in Figure 7. During the forced lane change, the driver must observe the road environment and confirm that the environment can allow the target vehicle to make a safe lane change. When the target vehicle and the rear adjacent vehicle meet the conditions required for a lane change, the participant will quickly change to the target lane. As shown in Figure 7, when the subject drives the vehicle so that it approaches the target lane to change lanes, it is necessary to assess whether there is any risk in the traffic environment around the target lane. When the current lane change is too risky, the lane change cannot be completed, and the participant needs to return the vehicle to the center of the current lane. The waiting lane strategy is shown in Figure 7. When the driver assesses the traffic environment during the process of a lane change, the driver can change lanes when the vehicle behind the target lane no longer poses a threat to the target vehicle. The characteristic parameters such as lane change duration, aisle time, distance from lane line, steering wheel angle, relative distance, and relative speed differ for different lane change types. The random forest classifier is used to construct the lane change recognition model, and the recognition performance of the attention-bidirectional long short-term memory (attenbilstm) model and the genetic algorithm-support vector machine (GA-SVM) model is compared. The experimental results show that the global recognition accuracy and recognition time under different input parameters are better than with the GA-SVM model, and the performance of the attention-bidirectional long short-term memory (attenbilstm) model is comparable. Table 8 gives a summary of the information-processing models. \begin{table}
\begin{tabular}{c c c c} \hline
**Paper** & **Input Variables** & **Algorithm** & **Key Issues to Be Resolved** \\ \hline
[129] & NGSIM dataset & FIS + LSTM & The influence of the driver and driving environment on lane change behavior is taken into account \\ \hline
[132] & Driver complex behavior and motivation characteristics & FCM & Discover the parameters that prompt the driver to change lanes \\ \hline
[133] & Driver physiological information and vehicle dynamic data & HMM & A method to predict driving risk during lane change using driver physiological measurement data and vehicle dynamic data \\ \hline
[134] & Under different lane change strategies, lane change duration, aisle time, distance from the lane line, steering wheel angle, relative distance, relative speed, and other characteristic parameters & Random forest classifier & Find out the different lane change strategies of the subject vehicle when there is a rear-approaching vehicle in the target lane \\ \hline \end{tabular}
\end{table} 
Table 8: Input parameters of different information-processing lane change models and key problems to be solved.