no code implementations • 1 Apr 2024 • Sourav Biswas, Sergio Casas, Quinlan Sykora, Ben Agro, Abbas Sadat, Raquel Urtasun
Instead, we shift the paradigm to have the planner query occupancy at relevant spatio-temporal points, restricting the computation to those regions of interest.
no code implementations • CVPR 2021 • 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.
no code implementations • CVPR 2021 • John Phillips, Julieta Martinez, Ioan Andrei Bârsan, Sergio Casas, Abbas Sadat, Raquel Urtasun
Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning.
1 code implementation • CVPR 2019 • Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, Raquel Urtasun
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 interactions with multiple road-users.
no code implementations • 16 Jan 2021 • Abbas Sadat, Sean Segal, Sergio Casas, James Tu, Bin Yang, Raquel Urtasun, Ersin Yumer
Our experiments on a wide range of tasks and models show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
no code implementations • CVPR 2021 • Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, Raquel Urtasun
Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack.
no code implementations • ICCV 2021 • Alexander Cui, Sergio Casas, Abbas Sadat, Renjie Liao, Raquel Urtasun
In this paper, we present LookOut, a novel autonomy system that perceives the environment, predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of the SDV by optimizing a set of contingency plans over these future realizations.
no code implementations • 12 Nov 2020 • Sean Segal, Eric Kee, Wenjie Luo, Abbas Sadat, Ersin Yumer, Raquel Urtasun
In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data.
no code implementations • ECCV 2020 • Abbas Sadat, Sergio Casas, Mengye Ren, Xinyu Wu, Pranaab Dhawan, Raquel Urtasun
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 intermediate representations.
no code implementations • ECCV 2020 • Kelvin Wong, Qiang Zhang, Ming Liang, Bin Yang, Renjie Liao, Abbas Sadat, Raquel Urtasun
We present a novel method for testing the safety of self-driving vehicles in simulation.
no code implementations • 10 Oct 2019 • Abbas Sadat, Mengye Ren, Andrei Pokrovsky, Yen-Chen Lin, Ersin Yumer, Raquel Urtasun
The motion planners used in self-driving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules.