期刊
IECON 2020: THE 46TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
卷 -, 期 -, 页码 5057-5062出版社
IEEE
DOI: 10.1109/iecon43393.2020.9255162
关键词
Trajectory prediction; autonomous driving; NGSIM
类别
资金
- SUG-NAP Grant of Nanyang Technological University, Singapore [M4082268.050]
Predicting the future trajectory of a surrounding vehicle in congested traffic is one of the necessary abilities of an autonomous vehicle. In congestion, a vehicle's future movement is the result of its interaction with surrounding vehicles. A vehicle in congestion may have many neighbors in a relatively short distance, while only a small part of neighbors affect its future trajectory mostly. In this work, An interaction-aware method that predicts the future trajectory of an ego vehicle considering its interaction with eight surrounding vehicles is proposed. The dynamics of vehicles are encoded by LSTMs with shared weights, and the interaction is extracted with a simple CNN. The proposed model is trained and tested on trajectories extracted from the publicly accessible NGSIM US-101 dataset. Quantitative experimental results show that the proposed model outperforms previous models in root-mean-square error (RMSE). Results visualization shows that the model is able to predict future trajectory induced by lane change before the vehicle operates noticeable lateral movement to initiate lane changing.
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