351 citations · 370 across the 6 of their papers we have counts for
8 papers
GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting
Alexander Cui, Sergio Casas, Kelvin Wong +2
The task of motion forecasting is critical for self-driving vehicles (SDVs) to be able to plan a safe maneuver. Towards this goal, modern approaches reason about the map, the agent…
Deep Parametric Continuous Convolutional Neural Networks
Shenlong Wang, Simon Suo, Wei-Chiu Ma +2
Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This limits their applica…
End-to-end Interpretable Neural Motion Planner
Wenyuan Zeng, Wenjie Luo, Simon Suo +4
In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interaction…
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
Simon Suo, Sebastian Regalado, Sergio Casas +1
Simulation has the potential to massively scale evaluation of self-driving systems enabling rapid development as well as safe deployment. To close the gap between simulation and th…
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…
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…