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

• URL: http://arxiv.org/abs/2008.05927v1
• Date: Thu, 13 Aug 2020 14:30:12 GMT
• Title: End-to-end Contextual Perception and Prediction with Interaction Transformer
• Authors: Lingyun Luke Li, Bin Yang, Ming Liang, Wenyuan Zeng, Mengye Ren, Sean Segal, Raquel Urtasun
• Abstract summary: We tackle the problem of detecting objects in 3D and forecasting their future motion in the context of self-driving. To capture their spatial-temporal dependencies, we propose a recurrent neural network with a novel Transformer architecture. Our model can be trained end-to-end, and runs in real-time.
• Score: 79.14001602890417
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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