# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 1 Introduction
[15] designed a full-speed-range ACC system using the fuzzy control theory. The genetic algorithm, particle swarm algorithm, and differential evolution algorithm have been used to optimize the parameters of the fuzzy controller. The designed ACC system can be used in urban traffic and highways. Considering the time-consuming problem of adaptive control design, caused by the combination of different characteristics and situation-dependent behaviors, [PERSON] et al. [16] proposed a systematic method of parameterized adaptive control design, based on explicit model predictive control. [PERSON] et al. [17] investigated the driving state of a driver when the driver did not use the ACC function for comparison experiments, and collected information, such as acceleration and vehicle spacing, which was taken in the form of observations of lane changes. In addition to the study of ACC control strategies, many studies have taken the driving characteristics of a driver in the design of ACC into account. [PERSON] et al. [18] developed an adaptive longitudinal driving assistance system based on driver characteristics using the recursive least squares method to identify the parameters of the driver model based on manual operation, and applied those parameters to real-time control. [PERSON] et al. [19] used a set of offline driving data to teach a driver model instead of a human driver, and proposed a new intelligent longitudinal speed control (LVC) framework, which included a driver model and a model predictive controller. [PERSON] et al. [20] applied a feature-based demonstration learning method, in which the driver model is trained to produce a driving trajectory, similar to that shown by a driver, during vehicle following. [PERSON] et al. [21] designed a self-learning driver model that used the hidden Markov model (HMM) and Gaussian mixture model (GMM) to identify and predict a driver's steering intention and acceleration, combining it with the MPC algorithm to design an ACC system controller. The system could adapt to different types of drivers and ensure driving safety. [PERSON] et al. [22; 23] also designed different assisted driving strategies for ADASs, based on different types of drivers. [PERSON] et al. [24] proposed a full-speed ACC; by activating the electronic control unit (ECU), they used a few minutes of the online learning mode to learn a driver's preferences and attitudes, and then updated the parameters to achieve a full-speed ACC that conformed to the driver's behavior characteristics. Although the above papers show various studies on ACCs, the cut-in scenarios are still a challenge. Therefore, an ACC control strategy needs to consider cut-in possibility, and controls a vehicle to make a decision accordingly, which is meaningful to improve ACC driving safety and comfort. The ACC for cut-in scenarios based on the model predictive control algorithm in this paper is structurally illustrated in Figure 1. To begin this process, a finite state machine (FSM) was adopted to introduce different cut-in scenarios, which generated a reference trajectory for vehicle control. The second method used to recognize potential cut-in vehicles and to quantify the cut-in possibility based on nearby vehicle states was proposed. By comparing, judging the position of a potential cut-in vehicle with the position of the host vehicle, a potential cut-in vehicle on both sides of a lane was determined. Through analysis of the lateral displacement and lateral velocity of a potential cut-in vehicle, the cut-in possibility of a potential cut-in vehicle was quantified and then used as the reference for MPC to realize a coordinated control between the host vehicle and the cut-in vehicle. In addition, considering a straight lane and a curved lane, a safety distance model of the cut- in vehicle was proposed in this paper.