# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Cognitive Models of Lane Change
#### 2.3.3 Information-Processing Model
The lane change feasibility and vehicle trajectory are predicted as input variables of the LSTM neural network, and then the decision strategy of path planning is designed based on the experimental results. The experiment used an NGSIM dataset for training and testing, and the results showed that this method can improve the ability to process lane changes. The needs and intentions of road users play an important role in improving the autonomous recognition of driver assistance systems and the assessment of driving situations. In order to effectively predict lane changes, the characteristics of different situations and drivers must be taken into account. [PERSON] et al. [130] proposed an algorithm to predict lane changes by evaluating driving conditions, driver behavior, and vehicle motion. This algorithm makes high demands on the perception of the vehicle's driving environment, combined with the analysis of the driver's observation behavior of the environment before changing lanes. The artificial neural network, parameterized via machine learning, is used to fuse the parameter information and use it for classification. Driving behavior prediction technology is of great significance to the optimization of advanced driver assistance systems (ADASs). [PERSON] et al. [131] proposed a driving behavior prediction method based on the hidden Markov model (HMM). The three different driving actions of left lane changing, right lane changing, and lane keeping were the hidden states of the hidden Markov model (HMM). On the basis of observation (training), the hidden Markov model (HMM) method could calculate the most likely driving behavior from the observed sequence. Observation sequences were also used to train the HMM. In order to improve the performance of the model, the collected signals were quantified into observation sequences with specific features. Compared with other methods, the validity of the driving behavior prediction was proven. The process of autonomous lane changing is mainly divided into three stages: perception, decision, and execution. In the first stage, a lot of environmental information is collected; before changing lanes, the driver must first observe the changes in the surrounding driving environment. In the second stage, the lane change decision is influenced by the driving environment parameters around the vehicle. At this stage, there are some parameters that influence the lane change decision and some parameters that influence the driver's lane change. The study by [PERSON], [PERSON] et al. [132] aimed to investigate the parameters that influence drivers' lane changes. In that study, the parameters determined by interviews with drivers were verified, and the significant association feature extraction (SigAFE) algorithm was used to extract the parameters influencing lane change decisions. Using the influence value of lane change decisions, the fuzzy cognition graph (FCM) model of arbitrary lane change decisions was developed. In lane change decision-making, drivers need to observe the traffic conflicts in both the current lane and the target lane. [PERSON] et al. [133] used driver physiological data and vehicle dynamic data to predict driving risks during lane changes. The data from the experiment were obtained from a wearable sensor assessing the actual driving process. The hidden Markov model was used to correlate driving risk with driver physiological information and vehicle dynamic data. The effects of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk were compared. The standard deviation of the normal to normal R-R interval of heart rate (SDNN), fixation time, sash-up range, and average speed were selected as inputs to the hidden Markov model. The model uses data from physiological measurement sensors to predict the transition probability between hazardous driving states and normal driving states. A multi-dimensional information interaction platform improves the identification of specific lane-changing strategies.