Interaction-aware Kalman neural networks for trajectory prediction

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Forecasting the motion of surrounding dynamic obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for autonomous vehicles. Complex traffic scenes yield great challenges in modeling the traffic patterns of surrounding dynamic obstacles. In this paper, we propose a multi-layer architecture Interaction-aware Kalman Neural Networks (IaKNN) which involves an interaction layer for resolving high-dimensional traffic environmental observations as interaction-aware accelerations, a motion layer for transforming the accelerations to interaction-aware trajectories, and a filter layer for estimating future trajectories with a Kalman filter. Experiments on the NGSIM dataset demonstrate that IaKNN outperforms the state-of-the-art methods in terms of effectiveness for trajectory prediction.
Publisher
IEEE
Issue Date
2020-11-01
Language
English
Citation

31st IEEE Intelligent Vehicles Symposium

URI
http://hdl.handle.net/10203/278535
Appears in Collection
EE-Conference Papers(학술회의논문)
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