# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Physical Models of Lane Change
#### 2.2.3 Control Models  
A vehicle control model mainly refers to the influence of the vehicle control system and control strategy on the lane change process, and describes the connection between control input (such as steering angle, brake, and accelerator pedal) and vehicle lane change, so as to study the influence of the control system on lane change. With the continuous development of vehicle automation, the demand for control technology is also increasing. There are two main directions of vehicle control technology: the first is optimization-based control, which summarizes the driving task as an objective function of multiple constraints' minimization or maximization and then solves it [97; 98]. The second is intelligence-based control, which is mainly based on deep learning, reinforcement learning, and other AI-related techniques, and mainly uses the function approximation capabilities of deep neural network models to solve complex decision processes. Intelligent-control-related models have been applied to traffic flow prediction [99; 100; 101; 102]. [PERSON] et al. [103] adopted the fusion of random forest and AdaBoost to make lane change decisions via the first lane change model established through NGSIM. The test results show that the integrated learning method of random forest and AdaBoost can obtain higher classification accuracy than Bayesian or decision tree classifiers. [PERSON] et al. [104] proposed that a lane-changing vehicle should constantly adjust its driving speed and position according to the environment, to ensure safety according to the dynamically changing objects around the vehicle. In order to solve this problem, a dynamic lane-changing trajectory planning model was developed that is composed of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Input Variables** & **Algorithm** & **Key Issues to Be Resolved** \\ \hline
[91] & Traffic flow, lane change frequency, and vehicle speed & Microscopic LCP model & Can handle non-continuous lane change processes \\ \hline
[92] & Traffic efficiency, traffic safety, fuel consumption, including emergency sign distance before lane closure, speed limit, traffic density, etc. & Linear regression model of the logarithmic lane changing & Additional lane change rules are proposed to deal with movement bottleneck and lane reduction, and the impact of lane change on traffic efficiency, traffic safety, and fuel consumption is studied \\ \hline
[93] & Vehicle lane change time, vertical and horizontal speed, and acceleration are key variables & Genetic algorithm-back propagation neural network & It improves the quasi-human and real-time performance of intelligent vehicle lane change trajectory planning \\ \hline
[96] & Vehicle number 689 in the NGSIM data & PSO-BP neural network + genetic algorithm & Safe and efficient lane changes can be planned for vehicles in real-time \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Input parameters of different dynamic lane change models and key problems to be solved. Figure 3: Structure of particle swarm optimization (PSO)-BP neural network [96]. lane-changing starting point determination module, the trajectory decision module, and the trajectory generation module. It avoids making assumptions of lane change speed and acceleration that are unrealistic in the current vehicle lane change model. [PERSON] et al. [105] studied the lane change trajectory-planning model of probabilistic prediction combined with deterministic prediction for automatic driving in a complex traffic environment, and established a stochastic model predictive control problem to obtain the steering angle and longitudinal acceleration of vehicles under constraints. [PERSON] et al. [106] proposed a data-driven lane change model based on deep learning, which a combined deep belief network (DBN) and long short-term memory (LSTM) neural network, and modeled the lane change process data provided by the dataset (NGSIM) with the combined network model. [PERSON] et al.