In a cut-in scenario, traditional adaptive cruise control usually cannot effectively identify the cut-in vehicle and respond to it in advance. This paper proposes an adaptive cruise control (ACC) strategy based on the MPC algorithm for cut-in scenarios. A finite state machine (FSM) is designed to manage vehicle control in different cut-in scenarios. For a cut-in scenario, a method to identify and quantify the possibility of cut-in of a vehicle is proposed. At the same time, a safety distance model of the cut-in vehicle is established as the basis for the state transition of the finite state machine. Taking the quantified cut-in possibility of a vehicle as a reference, the model predictive control (MPC) algorithm, which considers the constraints of driving safety and comfort, is used to realize coordinated control of the host vehicle and the cut-in vehicle. Simulink–Carsim simulation studies show that the ACC strategy for a cut-in scenario can effectively identify a cut-in vehicle and quantify the possibility of cut-in of the vehicle. Faced with a cut-in vehicle, the host vehicle using the ACC strategy can brake several seconds early and switch the following target to the cut-in vehicle. Meanwhile, the acceleration and jerk of the host vehicle changes within a reasonable range, which ensures driving safety and comfort.

For intelligent vehicles, trajectory tracking control is of vital importance. However, due to the cut-in possibility of adjacent vehicles, trajectory planning of intelligent vehicles is challenging. Therefore, this paper proposes a trajectory tracking control method based on cut-in behavior prediction. A method of cut-in intention recognition is adopted to judge the possibility of adjacent vehicle and the driver preview model is used to predict the trajectory of the cut-in vehicle. The three driving scenarios are divided to manage trajectory planning under different cut-in behaviors. At the same time, the safety distance model is established as the basis for scene conversion. Taking the predicted trajectory of the cut-in vehicle as a reference, the model predictive control (MPC) method is used to plan and control the driving trajectory of the subject vehicle, so as to realize the coordinated control of the subject vehicle and the cut-in vehicle. Finally, the simulation shows that the subject vehicle can effectively recognize the cut-in intention of the adjacent vehicle and predict its trajectory. Facing with the cut-in vehicle, the subject vehicle can take appropriate control actions in advance to ensure the safety. Finally, a smoother coordinate control process is obtained between the subject vehicle and the cut-in vehicle.

<p>Efficient and accurate prediction of surrounding vehicles' trajectories over time is crucial for autonomous vehicle decision-making and planning. While the Transformer method has been widely used for interactive vehicle trajectory prediction due to its ability to consider multi-vehicle trajectories in parallel, this parallel computation mechanism causes computational exponential overload in Long Sequence Time-series Forecasting (LSTF) problems. In response, this paper proposes an improved Transformer method, known as the structural Informer, which can achieve accurate and efficient long-term trajectory prediction of the target vehicle (TV). Specifically, the proposed method considers not only the temporal and spatial features of the interaction trajectory, but also the impact of vehicle state changes on the trajectory. To reduce computational redundancy and complexity while improving memory usage and prediction accuracy, the <em>ProbSparse</em> self-attention mechanisms and attention distillation operations are employed. The method is validated and evaluated using the NGSIM dataset, and the results demonstrate that the proposed structural Informer achieves satisfactory accuracy and time cost in long-term prediction of the TV compared with various interactive trajectory prediction methods.</p>

<p>Efficient and accurate prediction of surrounding vehicles' trajectories over time is crucial for autonomous vehicle decision-making and planning. While the Transformer method has been widely used for interactive vehicle trajectory prediction due to its ability to consider multi-vehicle trajectories in parallel, this parallel computation mechanism causes computational exponential overload in Long Sequence Time-series Forecasting (LSTF) problems. In response, this paper proposes an improved Transformer method, known as the structural Informer, which can achieve accurate and efficient long-term trajectory prediction of the target vehicle (TV). Specifically, the proposed method considers not only the temporal and spatial features of the interaction trajectory, but also the impact of vehicle state changes on the trajectory. To reduce computational redundancy and complexity while improving memory usage and prediction accuracy, the <em>ProbSparse</em> self-attention mechanisms and attention distillation operations are employed. The method is validated and evaluated using the NGSIM dataset, and the results demonstrate that the proposed structural Informer achieves satisfactory accuracy and time cost in long-term prediction of the TV compared with various interactive trajectory prediction methods.</p>