Search Results for author: Sergio Casas

Found 18 papers, 0 papers with code

Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes

no code implementations8 Apr 2021 Sean Segal, Nishanth Kumar, Sergio Casas, Wenyuan Zeng, Mengye Ren, Jingkang Wang, Raquel Urtasun

As data collection is often significantly cheaper than labeling in this domain, the decision of which subset of examples to label can have a profound impact on model performance.

Active Learning

IntentNet: Learning to Predict Intention from Raw Sensor Data

no code implementations20 Jan 2021 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.

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.

End-to-end Interpretable Neural Motion Planner

no code implementations 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.

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

TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

no code implementations CVPR 2021 Simon Suo, Sebastian Regalado, Sergio Casas, Raquel Urtasun

We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.

Common Sense Reasoning Data Augmentation

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

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

Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting

no code implementations7 Jan 2021 Katie Luo, Sergio Casas, Renjie Liao, Xinchen Yan, Yuwen Xiong, Wenyuan Zeng, Raquel Urtasun

On two large-scale real-world datasets, nuScenes and ATG4D, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods, while also matching their performance in pedestrian motion forecasting metrics.

Motion Forecasting

StrObe: Streaming Object Detection from LiDAR Packets

no code implementations12 Nov 2020 Davi Frossard, Simon Suo, Sergio Casas, James Tu, Rui Hu, Raquel Urtasun

In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built.

object-detection Object Detection

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

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

no code implementations ECCV 2020 Bin Yang, Runsheng Guo, Ming Liang, Sergio Casas, Raquel Urtasun

We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the form of Doppler velocity.

object-detection Object Detection

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

no code implementations ECCV 2020 Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, Raquel Urtasun

In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants.

Motion Forecasting Motion Planning

The Importance of Prior Knowledge in Precise Multimodal Prediction

no code implementations4 Jun 2020 Sergio Casas, Cole Gulino, Simon Suo, Raquel Urtasun

Towards this goal, we design a framework that leverages REINFORCE to incorporate non-differentiable priors over sample trajectories from a probabilistic model, thus optimizing the whole distribution.

Motion Forecasting Motion Planning

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