Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

Sergio Casas; Cole Gulino; Renjie Liao; Raquel Urtasun

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arXiv · 1910.08233

Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

Sergio Casas·Cole Gulino·Renjie Liao·Raquel Urtasun

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Abstract

In this paper, we tackle the problem of relational behavior forecasting from sensor data. Towards this goal, we propose a novel spatially-aware graph neural network (SpAGNN) that models the interactions between agents in the scene. Specifically, we exploit a convolutional neural network to detect the actors and compute their initial states. A graph neural network then iteratively updates the actor states via a message passing process. Inspired by Gaussian belief propagation, we design the messages to be spatially-transformed parameters of the output distributions from neighboring agents. Our model is fully differentiable, thus enabling end-to-end training. Importantly, our probabilistic predictions can model uncertainty at the trajectory level. We demonstrate the effectiveness of our approach by achieving significant improvements over the state-of-the-art on two real-world self-driving datasets: ATG4D and nuScenes.

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BibTeX RIS

Sergio Casas, Cole Gulino, Renjie Liao, Raquel Urtasun. 2019-10-18. Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data. https://arxiv.org/abs/1910.08233

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