# Vehicle Lane Change Models--A Historical Review
## 1 Introduction  
Driving vehicles on the road is a mode of daily transport, but driving vehicles on roads with heavy traffic will spawn a variety of unexpected conditions, such as emergency lane changes, emergency braking, and so on, resulting in a series of traffic accidents. Lane change behavior has a significant impact on traffic flow during commuting, as well as driving planning and driving safety. Lane changing is a common traffic behavior in the process of driving and an indispensable part of driving behavior. It mainly refers to the process of the target vehicle moving from the driving lane to another target lane. The correct operation of lane change behavior will enhance the driver's sense of driving experience, improve the efficiency of traffic flow during commutes, and avoid dangerous traffic conditions ahead of time. On the contrary, drivers changing lanes dangerously often causes traffic flow turbulence and may even lead to traffic accidents [1; 2]. Traffic accidents caused by lane changes account for 5% of the total number of accidents, including 75% caused by drivers changing lanes dangerously [3]. Statistics from the National Highway Traffic Safety Administration (NHTSA) show that approximately 240,000 to 610,000 traffic accidents are caused by drivers changing lanes each year, and at least 60,000 people are injured in traffic accidents caused by lane changes [4]. In 2019, a total of 10,706 traffic accidents were caused by overtaking and lane changing, accounting for 4.32% of the total traffic accidents in China [5]. Therefore, the prediction of lane change risk is essential to improve road commuting, travel planning, and traffic safety. The decisions involved in changing lanes on roads with heavy traffic are complex and dynamic. Lane change behavior involves the surrounding vehicles. According to interactions with surrounding vehicles, lane change behavior can be divided into two categories: voluntary and involuntary. Voluntary lane changing is an active lane change behavior based on the driver's own will, such as overtaking and lane changing. Involuntarylane changing is a behavior in which drivers are forced to change lanes under the influence of external environmental factors, such as the avoidance of obstacles. In addition, lane change behavior is also affected by a variety of factors, such as driver characteristics (gender, driving experience, age, etc. ), different traffic environments (urban roads, expressways, township roads, etc. ), vehicle dynamic performance, and the differences between morning, afternoon, and evening travel times. Dangerous and emergency lane changes will cause instability for the vehicles behind the target vehicle, indirectly increasing the probability of traffic congestion and accidents [6]. Currently, the index type of traditional lane change prediction model training is relatively simple, and the parameters considered in model training are not comprehensive, meaning there is a need to further improve the accuracy and real-time performance of prediction models. In fact, the relevant indicators of vehicles in the process of lane changing are related, and training models with various reference indicators can effectively improve the detection accuracy of lane change models. Lane change models are usually composed of vehicle dynamic parameters, vehicle driving states, and driver characteristic indicators. The vehicle dynamic parameters include the steering wheel angle, lateral speed, steering speed, and brake pedal position [7; 8; 9], and the driver's characteristic indicators include head movement, eye movement, limb movement, and EEG changes [10; 11; 12; 13]. The driving state of the vehicle includes speed, acceleration, driving direction, and other factors [14; 15; 16; 17]. If the driver wants to display a reasonable lane change behavior at the time of a lane change, they need to carry out accurate lane change judgment and lane change processing.