Accurate and efficient prediction of the future trajectories of surrounding vehicles is of utmost importance in motion planning for autonomous driving. The ability to predict longer-term trajectories provides valuable information for effective motion planning. Numerous studies have contributed to the prediction of long-term vehicle trajectories. However, it is important to note that longer-term predictions can potentially lead to a trade-off between accuracy and computational complexity. In this work, we propose a structural Informer method, which can achieve accurate and efficient long-term trajectory prediction of the target vehicle. Specifically, the proposed method considers not only the temporal and spatial features of the interaction vehicle 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 ProbSparse 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 state-of-the art methods Note to Practitioners —The motivation of this research is to address the impact of future trajectories of surrounding vehicles on the motion planning of autonomous vehicles. The method proposed applies advanced deep learning methods, and its strongpoint is that the proposed network can achieve higher efficiency and accuracy of trajectory prediction compared with state-of-the art methods. The specific implementation method is to use structured embedding methods and networks to extract more valuable features of the target vehicle, such as spatiotemporal features and vehicle state features. The novel attention mechanism is designed to solve the problem of exponential growth in computational complexity of traditional attention mechanisms in long-term prediction. The advancement of the method proposed confirmed by verification on naturalistic driving dataset.

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.

To facilitate intelligent vehicles in making informed decisions and plans, the precise and efficient prediction of vehicle trajectories is imperative. However, the future trajectory of a vehicle is not solely determined by its own historical path; it is also influenced by neighboring vehicles (NVs). Hence, understanding the interactions between vehicles is crucial for trajectory prediction. Additionally, the computational challenges posed by long sequence time-series forecasting (LSTF) add complexity to trajectory prediction tasks. This paper introduces a novel network, named Sparse Attention Graph Convolution Network (SAGCN), designed to comprehensively consider the trajectory interaction details of multiple vehicles, optimizing the LSTF for the target vehicle (TV). Specifically, grounded in real-world driving scenarios and vehicle interaction nuances, a multi-vehicle topology graph is formulated to amalgamate the historical trajectories of the TV and the interaction trajectories of NVs. The SAGCN network employs the Graph Convolutional Network (GCN) to assimilate and analyze diverse features within the multi-vehicle topology graph, subsequently computing the future trajectory of the vehicle through a sparse attention mechanism. The proposed method is validated and evaluated using natural datasets. The results demonstrate that, in comparison to state-of-the-art methods, the SAGCN network presented attains exceptional prediction accuracy and satisfactory time efficiency when predicting the trajectories of TV in LSTF.

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.

To facilitate intelligent vehicles in making informed decisions and plans, the precise and efficient prediction of vehicle trajectories is imperative. However, a vehicle's future trajectory is not solely determined by its own historical path; it is also influenced by neighboring vehicles (NVs). Hence, understanding the interactions between vehicles is crucial for trajectory prediction. Additionally, the computational challenges posed by long sequence time-series forecasting (LSTF) add complexity to trajectory prediction tasks. This paper introduces a novel network, named Sparse Attention Graph Convolution Network (SAGCN), designed to comprehensively consider the trajectory interaction details of multiple vehicles, optimizing the LSTF for the target vehicle (TV). Specifically, grounded in real-world driving scenarios and vehicle interaction nuances, a multi-vehicle topology graph is formulated to amalgamate the historical trajectories of the TV and the interaction trajectories of NVs. The SAGCN network employs the Graph Convolutional Network (GCN) to assimilate and analyze diverse features within the multi-vehicle topology graph, subsequently computing the future trajectory of the vehicle through a sparse attention mechanism. The proposed method is validated and evaluated using natural datasets. The results demonstrate that, in comparison to state-of-the-art methods, the SAGCN network presented attains exceptional prediction accuracy and satisfactory time efficiency when predicting the trajectories of TV in LSTF.