It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the interaction between vehicles must be considered. Interaction aware prediction methods track the previous trajectories of both the target vehicle and its surrounding vehicles. In this study, the authors consider trajectory prediction as a sequence-to-sequence prediction problem. They tackle this problem with an LSTM encoder–decoder framework. Moreover, they propose two spatial-attention mechanisms to account for the interaction between vehicles, i.e. context attention and lane attention. Spatial-attention mechanisms adopt the selective-attention mechanism of human drivers. They choose context vectors to help the model understand the surrounding environment better and thus improve its prediction accuracy. They evaluate the authors’ methods on the highD data set recorded in German highways with root mean squared error metric. Their experimental results show superior performance to other state-of-the-art methods. Code is available at https://github.com/momo1986/Spatial-attention.
Trajectory prediction for intelligent vehicles using spatial-attention mechanism
IET Intelligent Transport Systems ; 14 , 13 ; 1855-1863
2021-01-06
9 pages
Article (Journal)
Electronic Resource
English
sequence-to-sequence prediction problem , road vehicles , prediction accuracy , selective-attention mechanism , previous trajectories , mean square error methods , context attention , learning (artificial intelligence) , lane attention , spatial-attention mechanism , autonomous vehicles , Spatial-attention mechanisms , interaction aware prediction methods , LSTM encoder–decoder framework , decoding , intelligent vehicles , traffic engineering computing , trajectory prediction , safe trajectory
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