Lane Change–Intention Inference and Trajectory Prediction of Surrounding Vehicles on Highways

Do, Jongyong; Han, Kyoungseok; Choi, Seibum B.

doi:10.1109/tiv.2023.3266102

Cited by 16 publications

(1 citation statement)

References 43 publications

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“…The task of predicting vehicle trajectories primarily involves generating future trajectory based on historical vehicle trajectory data. Extant research concerning vehicle trajectory prediction can be broadly categorized into three domains: model-based methods [5], behavioral intentionbased methods [6], and deep learning-based methods [7].…”

Section: Introductionmentioning

confidence: 99%

Sparse Attention Graph Convolution Network for Vehicle Trajectory Prediction

Chen,

Yang

et al. 2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%