316 citations · 648 across the 7 of their papers we have counts for
9 papers
Scaling Vision-based End-to-End Driving with Multi-View Attention Learning
Yi Xiao, Felipe Codevilla, Diego Porres +1
On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., st…
Learned Image Compression for Machine Perception
Felipe Codevilla, Jean Gabriel Simard, Ross Goroshin +1
Recent work has shown that learned image compression strategies can outperform standard hand-crafted compression algorithms that have been developed over decades of intensive resea…
Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction
Roger Girgis, Florian Golemo, Felipe Codevilla +5
Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently learn a representation that approximates the true…
Action-Based Representation Learning for Autonomous Driving
Yi Xiao, Felipe Codevilla, Christopher Pal +1
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creati…
Multimodal End-to-End Autonomous Driving
Yi Xiao, Felipe Codevilla, Akhil Gurram +2
A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressin…
Exploring the Limitations of Behavior Cloning for Autonomous Driving
Felipe Codevilla, Eder Santana, Antonio M. López +1
Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation…