Trajectory Forecasting on Temporal Graphs

• URL: http://arxiv.org/abs/2207.00255v1
• Date: Fri, 1 Jul 2022 08:11:22 GMT
• Title: Trajectory Forecasting on Temporal Graphs
• Abstract summary: Predicting future locations of agents in the scene is an important problem in self-driving cars. We propose a temporal graph representation to better capture the dynamics in traffic scenes. We show better results that can reach the state-of-the-art performance on the Argoverse benchmark.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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