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Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving.
- Source :
- IEEE Transactions on Intelligent Transportation Systems; Oct2022, Vol. 23 Issue 10, p17654-17665, 12p
- Publication Year :
- 2022
-
Abstract
- Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles spatial interactions using a graph convolutional network (GCN), and captures temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Besides, we propose a weighted adjacency matrix to describe the intensities of mutual influence between vehicles, and the ablation study demonstrates the effectiveness of our scheme. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM). Comparisons in three aspects, including prediction errors, model sizes, and inference speeds, show that our network can achieve state-of-the-art performance. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15249050
- Volume :
- 23
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Intelligent Transportation Systems
- Publication Type :
- Academic Journal
- Accession number :
- 160686573
- Full Text :
- https://doi.org/10.1109/TITS.2022.3155749