Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatially-Aware Graph Neural Networks for Relational Behavior
  Forecasting from Sensor Data   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.

29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph