# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Physical Models of Lane Change
#### 2.2.3 Control Models
[107], addressing the impact of a connected vehicle environment on forced lane change behavior, used the CARRS-Q advanced driving simulator for testing and analyzed various performance indicators in forced lane change. They did so by applying repeated measurement AN variance analysis and generalized estimation equation of a linear hybrid model, and they concluded that a connected vehicle environment improves the safety of forced lane change. [PERSON] et al. [108] used a deep learning model long and short memory network (LSTM) to simulate vehicle following and lane change behavior at the same time, proposed a hybrid retraining constraint method to optimize the LSTM network, and used the vehicle time series and memory effect characteristics of the NGSIM dataset to predict the lane change behavior, which improved the prediction accuracy of lane change. [PERSON] et al. [6] proposed a method that could predict the risk level of lane change before it was completed, and proposed an algorithm combining resample and machine learning classifier for key spatial sequence features of vehicles. The verification on the dataset NGSIM found that both feature stability and risk level showed significantly improved performance. [PERSON] et al. [109] proposed that the safety and mobility of intelligent vehicles can be improved within the environmental space of sharing traffic information and issuing control motion instructions. However, due to the combination and volatility of intelligent vehicle networks, there are certain difficulties in the actual control process, which cannot be effectively solved using conventional control technology. Therefore, algorithms based on deep reinforcement learning are proposed, using roadside cloud platforms or other control facilities. To overcome a limited detection range of sensors, the integration of short- and long-distance information in the driving environment can help autonomous vehicles detect the surrounding environmental information more comprehensively and thus ensure appropriate decision-making and safe and effective motion planning during lane changes. [PERSON] et al. [110] summarized the automatic driving control method, which was mainly used for the traffic information perceived in the nearby traffic environment. But how is it possible to integrate the nearby traffic information with the long-distance traffic information, as well as traffic information obtained with different time stamps? Currently, there is relatively little research on how to incorporate this traffic information to enhance the control of autonomous driving. [PERSON] et al. [110] proposed a method based on deep reinforcement learning, which made three main contributions. The first contribution was to propose a DRL-based model (using a modified Deep Sets procedure), which integrates the information obtained locally with the traffic state information obtained from the vehicle connectivity function. The second contribution was the development of an end-to-end network architecture to control lane changes by combining the perceived and distant road information collected by the autopilot, that is, obstacles near the autopilot and details of vehicles at a distance, and combining these data to optimize lane changes of the autopilot vehicle. The third contribution was an evaluation of the impact of traffic density on the lane change process, providing an indication of connectivity thresholds to ensure optimal vehicle performance. The reinforcement learning algorithm was used to verify the safety and efficiency of connected driving vehicles in the simulation environment, as shown in Figure 4 below. The agent feeds the state information of the exploration environment into the reinforcement learning algorithm, which promotes desirable and weakens undesirable driving behavior [111]. In the simulation process, the agent drives by observing the current state, receives feedback information from the environment, and reacts to positive and negative information.