no code implementations • 7 Feb 2023 • Yi Xiao, Felipe Codevilla, Diego Porres, Antonio M. Lopez
With only self-supervised training data, our model yields almost expert performance in CARLA's Nocrash metrics and could be rival to the SOTA models requiring large amounts of human labeled data.
no code implementations • 3 Nov 2021 • Felipe Codevilla, Jean Gabriel Simard, Ross Goroshin, Chris Pal
Compression that ensures high accuracy on computer vision tasks such as image segmentation, classification, and detection therefore has the potential for significant impact across a wide variety of settings.
1 code implementation • ICLR 2022 • Roger Girgis, Florian Golemo, Felipe Codevilla, Martin Weiss, Jim Aldon D'Souza, Samira Ebrahimi Kahou, Felix Heide, Christopher Pal
AutoBots can produce either the trajectory of one ego-agent or a distribution over the future trajectories for all agents in the scene.
1 code implementation • 21 Aug 2020 • Yi Xiao, Felipe Codevilla, Christopher Pal, Antonio M. Lopez
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems.
no code implementations • 7 Jun 2019 • Yi Xiao, Felipe Codevilla, Akhil Gurram, Onay Urfalioglu, Antonio M. López
On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals.
2 code implementations • ICCV 2019 • Felipe Codevilla, Eder Santana, Antonio M. López, Adrien Gaidon
Driving requires reacting to a wide variety of complex environment conditions and agent behaviors.
Ranked #13 on Autonomous Driving on CARLA Leaderboard
1 code implementation • ECCV 2018 • Felipe Codevilla, Antonio M. López, Vladlen Koltun, Alexey Dosovitskiy
We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics.
1 code implementation • 10 Nov 2017 • Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, Vladlen Koltun
We introduce CARLA, an open-source simulator for autonomous driving research.
7 code implementations • 6 Oct 2017 • Felipe Codevilla, Matthias Müller, Antonio López, Vladlen Koltun, Alexey Dosovitskiy
However, driving policies trained via imitation learning cannot be controlled at test time.
no code implementations • 6 Mar 2016 • Joel D. O. Gaya, Felipe Codevilla, Amanda C. Duarte, Paulo L. Drews-Jr, Silvia S. Botelho
Differently from the related work that only deal with a medium, we obtain generality by using an image formation model and a fusion of new image priors.