Search Results for author: Abbas Sadat

Found 10 papers, 1 papers with code

MP3: A Unified Model to Map, Perceive, Predict and Plan

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.

Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

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.

Motion Forecasting Motion Planning +1

End-to-end Interpretable Neural Motion Planner

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.

Diverse Complexity Measures for Dataset Curation in Self-driving

no code implementations16 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.

Active Learning Motion Forecasting +1

AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

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.

LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving

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.

Future prediction Motion Forecasting

Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs

no code implementations12 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.

Blocking TAG +1

Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations

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.

Motion Planning

Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles

no code implementations10 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.

Trajectory Planning

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