# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Mixed Models of Lane Change
[139] found that most of the research on L/C risk prediction did not include driver intention recognition, and the real-time performance of L/C in the process of risk prediction was difficult to guarantee. Therefore, considering the driving risk difference between the left lane (LCL) and right lane (LCR), a letter-of-credit risk prediction framework is proposed, which integrates the letter-of-credit driving intention identification module and the letter-of-credit risk prediction module, as shown in Figure 10.  
Drivers' lane change intention is influenced by several complex factors [106]. In the cited study, vehicle type, vehicle motion characteristics, speed difference, acceleration difference, distance between adjacent vehicles, safety, efficiency, and comfort characteristics were extracted as part of the drivers' lane change intention. A long short-term memory (LSTM) neural network was used to identify a driver's lane change intention. In order to ensure the effectiveness of that intention, vehicle track data on vehicle motion features were extracted for risk prediction. The risk of lane change was quantified using fault tree analysis, and the risk of collision with adjacent vehicles was also calculated [140]. The optical gradient intensifier (LGBM) algorithm [141] was then used to predict the risk of lane changes. Finally, the key characteristics of lane change risk were verified by using the HighD trajectory dataset. Changing lanes is a complex driving behavior. Assisting vehicles to understand and accurately predict the lane change process can help road users to better navigate the environment around the driving vehicle and thus avoid potential safety hazards during the lane change process. [PERSON] et al. [142] developed a lane change intention recognition (LC-IR) model and a lane change state prediction (LC-SP) model. An  
Figure 10: Credit risk prediction framework [139]. integrated temporal convolutional network (TCN-LSTM) of long- and short-term memory units was proposed, as shown in Figure 11 below. The LSTM network has the problem of gradient disappearance and an inability to perform parallel computations [143]. The temporal convolutional network TCN [144] can capture the time-scale dependencies in the input sequence through the causal convolutional layer. Therefore, a temporal convolutional network (TCN-LSTM) is established to collect long-term dependencies in sequence data. In addition, three multi-task models, MLT-LSTM, MLT-TCN, and MLT-LSTM-TCN [145; 146], were also established to compare the relationships between driving state variables, as shown in Figures 12-14. Figure 11: Integrated time convolutional network of long short-term memory units. From left to right are the input layer, the TCN network structure, the detailed structure of the TCN residual block, the detailed structure of the LSTM module, the fully connected layer and the network output layer [142]. \[r=\sigma\Big{(}x_{t+1}^{k}U^{t}+s_{t}^{k}W^{t}\Big{)} \tag{9}\]0 MLT-LSTM network structure [142]. Based on vehicle trajectory data, a framework for lane change intention recognition and driving state prediction (LC-IR-SP) was proposed. For experimental verification, 1023 vehicle trajectories were selected from the CitySim [147] dataset. [PERSON]'s coefficient was used to determine the relevant indicators. The experimental results showed that the TCN-LSTM model is better than the TCN and LSTM models in the classification of a driver's lane change intention. The performance of the three multi-task learning models is significantly better than that of the single-task learning model. Table 9 gives a summary of the mixed models. At present, although the mixed models improve the performance of the lane change model to a certain extent, they still need to be further optimized to meet the requirements of vehicle lane change.