End-to-end Contextual Perception and Prediction with Interaction Transformer

In this paper, we tackle the problem of detecting objects in 3D and forecasting their future motion in the context of self-driving. Towards this goal, we design a novel approach that explicitly takes into account the interactions between actors. To capture their spatial-temporal dependencies, we pro...

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Bibliographic Details
Published in:arXiv.org 2020-08
Main Authors: Lingyun Luke Li, Yang, Bin, Liang, Ming, Zeng, Wenyuan, Ren, Mengye, Segal, Sean, Urtasun, Raquel
Format: Article
Language:English
Subjects:
Datasets
Object recognition
Recurrent neural networks
Trajectory analysis
Online Access:Get full text
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Summary:In this paper, we tackle the problem of detecting objects in 3D and forecasting their future motion in the context of self-driving. Towards this goal, we design a novel approach that explicitly takes into account the interactions between actors. To capture their spatial-temporal dependencies, we propose a recurrent neural network with a novel Transformer architecture, which we call the Interaction Transformer. Importantly, our model can be trained end-to-end, and runs in real-time. We validate our approach on two challenging real-world datasets: ATG4D and nuScenes. We show that our approach can outperform the state-of-the-art on both datasets. In particular, we significantly improve the social compliance between the estimated future trajectories, resulting in far fewer collisions between the predicted actors.
ISSN:2331-8422