# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Cognitive Models of Lane Change
#### 2.3.2 Artificial Neural Network Model
[124] used three physiological signals of drivers to predict lane changes, namely electrocardiogram (ECG), electrodermal response (GSR), and respiratory rate (RR). On the basis of these, a new model of MTS-GCNN for time series (MTS) pattern classification was proposed. A new structure learning algorithm was proposed in the training phase of the MTS-GCNN model. Different from other classification methods, the MTS-GCNN model generates temporal and spatial features by extracting suitable internal structures through convolution and subsampling. The experimental results showed that the MTS-GCNN model performs better than other advanced models in terms of prediction accuracy. Environmental factors make for uncertain lane changing behavior, which may cause serious traffic accidents. Effectively detecting the driver's intention to change lanes can improve safety. [PERSON] et al. [125] proposed a lane change prediction model based on the fuzzy C-means clustering algorithm and adaptive neural network (FCMNN), and added a new prediction process: an unsupervised learning method classifies the distribution features of the original dataset into different clusters, and then a supervised learning approach optimizes the sub-neural network structure and weight parameters for each cluster or pattern. Compared with some traditional methods, the prediction performance and stability of this model are improved significantly. With the rapid development of automobile automation and intelligence, intelligent driving assistance functions occupy an increasingly important part of the human driving process. The mutual cooperation and understanding between intelligent vehicles and human drivers is the key to achieving collaborative driving and efficiency. [PERSON] et al. [126] designed a two-stage spatiotemporal inference learning framework for a driver's lane change intention, which consists of two parts. The first part is a driving behavior recognition system based on the deep convolutional neural network (CNN), which is mainly used to identify the driver's driving behavior and check the rearview mirror. Driver behavior features based on a deep convolutional neural network (CNN) are used to construct a driver intent inference model composed of recurrent neural network (RNN) and long short-term memory (LSTM) algorithms. Experimental results show that the prediction accuracy of this model is more than 91%. The lane change prediction model, gives its good performance, can help drivers better understand the driving environment and thereby improve road safety. [PERSON] et al. [127] designed a temporal convolutional network (TCN-ATM) model based on the attention mechanism, to detect a driver's intention to change lanes. The multi-task learning (MTL) framework was used to predict various vehicle status indicators during a lane change. In addition, based on the multi-task learning (MTL) framework, a lane change intention recognition and driving state prediction modeling framework (LC-IR-SP) was proposed, as shown in Figure 6.  
The LC-IR-SP is composed of two core modules: lane change intent recognition (LC-IR) and lane change state prediction (LC-SP). The former is a classification model used to identify whether a vehicle has the intention to change lanes to the left or right. When it recognizes a lane change intention, the lane change state prediction (LC-SP) module starts to predict the driving state of the lane-changing vehicle. The LC-SP module consists of independent multi-task and single-task learning models. The multi-task learning model is responsible for predicting relevant variables, while the single-task learning model predicts irrelevant variables. The experimental results show that the classification accuracy of the driver's lane change intention in the TCN model with the attention mechanism increases from 96.14% to 98.20%. Table 7 gives a summary of the models based on the artificial neural network.