# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Cognitive Models of Lane Change
#### 2.3.2 Artificial Neural Network Model  
The artificial neural network model uses the neural network to process the driver's lane change behavior. The artificial neural network mainly consists of three parts. The first part is the input layer of the neural network, through which traffic information and road information related to lane changing are transmitted to the network. The second part is the hidden layer. Traffic information and road information related to the lane change are passed through the hidden layer via the input layer. The hidden layer mainly processes the input information to obtain the desired results. The third part is the output layer, through which the results obtained from the hidden layer are output. In the lane change behavior model, each neuron in the output layer represents specific lane change behavior information. The neural network has a strong learning ability, generalization ability, information synthesis ability, and self-adaptability, and it offers a way for researchers to improve the lane change model. The lane-changing process is a relatively important maneuver during driving, which will cause a large number of traffic jams and vehicle collisions. [PERSON] et al. [121] found that the existing lane change model does not consider the uncertainty and perceptions involved in human behavior during the lane change process. The neural network can learn related uncertainties to improve the accuracy when predicting the lane change trajectory. For instance, a multi-layer perceptron (MLP) was trained on NGSIM data to predict the lane change trajectory of a vehicle. The experimental results showed that the perceptron (MLP) can accurately predict the future path of the discrete trajectory, but cannot predict the future path of the complete trajectory. [PERSON] et al. [122] used a backpropagation (BP) neural network to predict the validity of lane change trajectories. The driving simulator data and NGSIM data were smoothly preprocessed. The experimental results show that \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Input Variables** & **Algorithm** & **Key Issues to Be Resolved** \\ \hline
[104] & US-101 and I-80 segments of NGSIM data & Random Forest + AdaBoost & Improve the accuracy of safety-critical lane changes and reduce the incidence of traffic accidents \\ \hline
[117] & US-101 and I-80 segments of NGSIM data & Random Forest & It can accurately describe the lane change mode and complete driver behavior characteristics \\ \hline
[118] & Driving behavior + eye-tracking data & HMM + SVM + CN-N + RF & Help the driver judge when it is appropriate to turn left or right or maintain their direction \\ \hline
[119] & Relative velocity, relative acceleration, and potential energy & Extremely randomized decision trees + harmonic potential field method & Prediction of driving angle during lane change \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Input parameters of different decision tree lane change models and key problems to be solved. the BP neural network can accurately predict lane change behavior in urban traffic. The accurate prediction of driving behavior is very important for the performance of active safety systems. [PERSON] et al. [123] designed a lane change behavior prediction model based on natural road experiments. The time window of lane change intention is determined by extracting the visual features of the rearview mirror. A backpropagation neural network model was established to integrate the drivers' visual search behavior, driving conditions, vehicle operation behavior, and vehicle motion state. The experimental results showed that the lane change model can accurately predict the driver's lane change behavior at least 1.5 s in advance. In this regard, a real-world classification problem involves multiple variables and analyses in the time domain. Time series data have high dimensionality, large data volume, and constantly updated performance. [PERSON] et al.