End-to-end Contextual Perception and Prediction with Interaction Transformer
Lingyun Luke Li,Bin Yang,Ming Liang,Wenyuan Zeng,Mengye Ren,Sean Segal,Raquel Urtasun,Lingyun Luke Li,Bin Yang,Ming Liang,Wenyuan Zeng,Mengye Ren,Sean Segal,Raquel Urtasun
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 [1] architecture, which we call the Interac...
