# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Cognitive Models of Lane Change
#### 2.3.1 Decision Tree Model  
The decision tree model decomposes the driver's lane change behavior into decision nodes and condition judgment. According to the driver's situation and driving environment, they decide whether to change lanes according to the decision nodes and condition judgment in the decision tree model. [PERSON] et al. [112] applied the decision tree model and production rule algorithm commonly used in data mining to study the behavior of driving path selection. Compared with the artificial neural network algorithm, this algorithm has an advantage, which can help researchers to determine the correlation between explanatory variables and choice. [PERSON] et al. [113] further studied the sensitivity method of decision tree based on the work of [PERSON] et al. [112]. Soft discretization of continuous values in the optimized fuzzy decision tree can improve the classification of robustness, and the fuzzy decision tree assigns values by deciding on the degree of certainty of proposals generated with fuzzy reasoning. This feature makes it possible to solve multiple proposal problems. The experimental results were compared with the non-fuzzy adaptive path selection, and the prediction accuracy of the fuzzy decision tree model was higher. The decision tree induction method of [PERSON] et al. [114] is a type of machine learning algorithm that can build a path selection model. Decision trees can accommodate routing preferences, but \"sharp cutoff points\" can make the tree too sensitive, leading to misclassification. [PERSON] et al. [114] added fuzzy decision tree induction to reduce sensitivity. The fuzzy decision tree optimizes the discretization in the classical decision tree to soft discretization via induction, thus improving the flexibility of new case classification. [PERSON] et al. [115] developed a lane change assistance system to remind the driver of the safe distance when changing lanes safely. The lane change model is modeled by using the decision tree method and Bayesian classifier, and the NGSIM dataset is used to train the model. The model predicts whether drivers will merge as a function of input variables. By using the majority voting principle, the decision tree method and Bayesian classifier are combined into one classifier, and the best result is obtained. The decision-making process of lane changing means the driver easily makes mistakes, so the optimization of collision avoidance system modeling and prediction of lane change maneuvers have become the focuses of research. Networked vehicle data provide the possibility for accurate lane change modeling. Such a system has high requirements for the accuracy of lane change initiation detection, and comprehensive vehicle lane change data can ensure that the lane change initiation accuracy requirements are met. Most lane change models of vehicles fail to achieve reliable accuracy in lane change prediction. [PERSON] et al. [116] chose the extreme gradient boosting (XGB) algorithm. Extreme gradient boosting (XGB) is a type of machine learning algorithm that can build a relatively strong learner by training multiple weak learners, generally a set of multiple decision trees. XGB uses the gradient lifting algorithm, which can iteratively train the decision tree model and use the connected vehicle data to predict the start of the lane change maneuver. The results obtained with the XGB model were compared with the decision tree, gradient lifting, and random forest, and the results showed that the XGB model performed better than the other algorithms. An advanced driver assistance system (ADAS) can warn drivers whether they are in a safe lane-changing environment according to the detected data. [PERSON] et al. [104] proposed integrating the random forest algorithm with the AdaBoost algorithm to improve the performance of the lane-changing assistance system. The experimental results show that the performance of the two integrated learning methods is better than that of the single learning algorithm model such as Bayes and the decision tree classifier.