Learning to Communicate and Correct Pose Errors
- URL: http://arxiv.org/abs/2011.05289v1
- Date: Tue, 10 Nov 2020 18:19:40 GMT
- Title: Learning to Communicate and Correct Pose Errors
- Authors: Nicholas Vadivelu, Mengye Ren, James Tu, Jingkang Wang, Raquel Urtasun
- Abstract summary: We study the setting proposed in V2VNet, where nearby self-driving vehicles jointly perform object detection and motion forecasting in a cooperative manner.
We propose a novel neural reasoning framework that learns to communicate, to estimate potential errors, and to reach a consensus about those errors.
- Score: 75.03747122616605
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Learned communication makes multi-agent systems more effective by aggregating
distributed information. However, it also exposes individual agents to the
threat of erroneous messages they might receive. In this paper, we study the
setting proposed in V2VNet, where nearby self-driving vehicles jointly perform
object detection and motion forecasting in a cooperative manner. Despite a huge
performance boost when the agents solve the task together, the gain is quickly
diminished in the presence of pose noise since the communication relies on
spatial transformations. Hence, we propose a novel neural reasoning framework
that learns to communicate, to estimate potential errors, and finally, to reach
a consensus about those errors. Experiments confirm that our proposed framework
significantly improves the robustness of multi-agent self-driving perception
and motion forecasting systems under realistic and severe localization noise.
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