250 citations · 354 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
IntentNet: Learning to Predict Intention from Raw Sensor Data
Sergio Casas, Wenjie Luo, Raquel Urtasun
In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high-level behavi…
MP3: A Unified Model to Map, Perceive, Predict and Plan
Sergio Casas, Abbas Sadat, Raquel Urtasun
High-definition maps (HD maps) are a key component of most modern self-driving systems due to their valuable semantic and geometric information. Unfortunately, building HD maps has…
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…
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…