TY - GEN
T1 - GISNet:Graph-Based Information Sharing Network for Vehicle Trajectory Prediction
AU - Zhao, Ziyi
AU - Fang, Haowen
AU - Jin, Zhao
AU - Qiu, Qinru
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber and DiDi, are investigating more accurate vehicle trajectory prediction algorithms. However, the prediction performance is governed by lots of entangled factors, such as the stochastic behaviors of surrounding vehicles, historical information of self-trajectory, and relative positions of neighbors, etc. In this paper, we propose a novel graph-based information sharing network (GISNet) that allows the information sharing between the target vehicle and its surrounding vehicles. Meanwhile, the model encodes the historical trajectory information of all the vehicles in the scene. Experiments are carried out on the public NGSIM US-101 and I-80 Dataset and the prediction performance is measured by the Root Mean Square Error (RMSE). The quantitative and qualitative experimental results show that our model significantly improves the trajectory prediction accuracy, by up to 50.00%, compared to existing models.
AB - The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber and DiDi, are investigating more accurate vehicle trajectory prediction algorithms. However, the prediction performance is governed by lots of entangled factors, such as the stochastic behaviors of surrounding vehicles, historical information of self-trajectory, and relative positions of neighbors, etc. In this paper, we propose a novel graph-based information sharing network (GISNet) that allows the information sharing between the target vehicle and its surrounding vehicles. Meanwhile, the model encodes the historical trajectory information of all the vehicles in the scene. Experiments are carried out on the public NGSIM US-101 and I-80 Dataset and the prediction performance is measured by the Root Mean Square Error (RMSE). The quantitative and qualitative experimental results show that our model significantly improves the trajectory prediction accuracy, by up to 50.00%, compared to existing models.
KW - ADS
KW - GNN
KW - Information Sharing
KW - Vehicle Tra-jectory Prediction
UR - http://www.scopus.com/inward/record.url?scp=85093836994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85093836994&partnerID=8YFLogxK
U2 - 10.1109/IJCNN48605.2020.9206770
DO - 10.1109/IJCNN48605.2020.9206770
M3 - Conference contribution
AN - SCOPUS:85093836994
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020
Y2 - 19 July 2020 through 24 July 2020
ER -