250 citations · 479 across the 19 of their papers we have counts for
6 papers · 1 filter
StrObe: Streaming Object Detection from LiDAR Packets
Davi Frossard, Simon Suo, Sergio Casas +3
Many modern robotics systems employ LiDAR as their main sensing modality due to its geometrical richness. Rolling shutter LiDARs are particularly common, in which an array of laser…
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
Abbas Sadat, Sergio Casas, Mengye Ren +3
In this paper we propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles and produces interpretable in…
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects
Bin Yang, Runsheng Guo, Ming Liang +2
We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the…
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
Sergio Casas, Cole Gulino, Simon Suo +3
In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants. In this paper, we aim t…
PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
Ming Liang, Bin Yang, Wenyuan Zeng +4
We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. Towards this goal we propose PnPNet, an end-to-end model that takes as inp…
The Importance of Prior Knowledge in Precise Multimodal Prediction
Sergio Casas, Cole Gulino, Simon Suo +1
Roads have well defined geometries, topologies, and traffic rules. While this has been widely exploited in motion planning methods to produce maneuvers that obey the law, little wo…