A cognition‐inspired trajectory prediction method for vehicles in interactive scenarios
نویسندگان
چکیده
Trajectory prediction of the ego vehicle is necessary for cooperation driving intelligent vehicles and drivers. Methods based on deep learning can fit complex functions, but they usually focus vehicles' behavioral characteristics. However, trajectories are closely related to cognition results Therefore, drivers' cognitive characteristics, a network model designed predict trajectories. Specifically, in perception stage, featured grids used that driver's view encode perceptual information; decision convolution graph attention operations combined interaction with surrounding traffic elements; motion elements constrained one hidden layer by actual control inputs design corresponding method obtain probabilistic results. With experiments two typical scenarios, including intersection roundabout, proposed reasonable accuracy generalizability. Meanwhile, abundant conducted compared, which reveal some common problems when predicting trajectories, particularly These lessons learned from this study summarized may be useful newcomers.
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ژورنال
عنوان ژورنال: Iet Intelligent Transport Systems
سال: 2023
ISSN: ['1751-9578', '1751-956X']
DOI: https://doi.org/10.1049/itr2.12345