Autonomous vehicles need to have the ability to predict the motion of surrounding vehicles, which will help to avoid potential accidents and make the best decision to ensure safety and comfort. The interactions among vehicles and those between them and the uncertainty of driving intention make trajectory prediction a challenging task. This study presents a long short-term memory (LSTM) model for the task of trajectory prediction to account for both the mutual information and the multi-modal intention. The model consists of a data fusion encoder and a multi-modal decoder. The data fusion encoder summarises the mutual information by multi-LSTM with shared parameters and the multi-modal decoder generates trajectories based on driving intention. In addition, mixture density network is added to output a probabilistic prediction which improves the reliability of prediction results. NGSIM data set is used for training and testing. The results show that the proposed model can better capture the interactive driving behaviour and outperforms the state-of-the-art methods in root-weighted square error of displacement and velocity.
Multi-modal vehicle trajectory prediction based on mutual information
IET Intelligent Transport Systems ; 14 , 3 ; 148-153
2020-02-17
6 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
driving intention , road vehicles , road safety , multimodal decoder , Gaussian processes , mutual information , long short-term memory model , multiLSTM , autonomous vehicles , interactive driving behaviour , decoding , multimodal intention , probabilistic prediction , NGSIM data set , road traffic , multimodal vehicle trajectory prediction , traffic engineering computing , data fusion encoder , object recognition , encoding , recurrent neural nets
Metadata by IET is licensed under CC BY 3.0
Multi-modal vehicle trajectory prediction method based on hierarchical order network
Europäisches Patentamt | 2023
|Interactive vehicle multi-modal trajectory prediction method based on GRU-GCN
Europäisches Patentamt | 2023
|