Trajectory Forecasting on Temporal Graphs
Not on the map. Why?
Abstract (arXiv)
Predicting future locations of agents in the scene is an important problem in self-driving. In recent years, there has been a significant progress in representing the scene and the agents in it. The interactions of agents with the scene and with each other are typically modeled with a Graph Neural Network. However, the graph structure is mostly static and fails to represent the temporal changes in highly dynamic scenes. In this work, we propose a temporal graph representation to better capture the dynamics in traffic scenes. We complement our representation with two types of memory modules; one focusing on the agent of interest and the other on the entire scene. This allows us to learn temporally-aware representations that can achieve good results even with simple regression of multiple futures. When combined with goal-conditioned prediction, we show better results that can reach the state-of-the-art performance on the Argoverse benchmark.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 37 of its 40 references that are in the map. First posted on arXiv 1 Jul 2022.
Builds on
37 of 40 references.
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25 more
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Citing papers per year: 2022: 1; 2023: 2.
3 papers.