Sergio Casas

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IntentNet: Learning to Predict Intention from Raw Sensor Data

In order to plan a safe maneuver, self-driving vehicles need to understa...
01/20/2021 ∙ by Sergio Casas, et al. ∙ 0 ∙ share

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

High-definition maps (HD maps) are a key component of most modern self-d...
01/18/2021 ∙ by Sergio Casas, et al. ∙ 14 ∙ share

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

Over the last few years, we have witnessed tremendous progress on many s...
01/17/2021 ∙ by John Phillips, et al. ∙ 0 ∙ share

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End-to-end Interpretable Neural Motion Planner

In this paper, we propose a neural motion planner (NMP) for learning to ...
01/17/2021 ∙ by Wenyuan Zeng, et al. ∙ 0 ∙ share

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TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

Simulation has the potential to massively scale evaluation of self-drivi...
01/17/2021 ∙ by Simon Suo, et al. ∙ 16 ∙ share

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Diverse Complexity Measures for Dataset Curation in Self-driving

Modern self-driving autonomy systems heavily rely on deep learning. As a...
01/16/2021 ∙ by Abbas Sadat, et al. ∙ 0 ∙ share

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

As self-driving systems become better, simulating scenarios where the au...
01/16/2021 ∙ by Jingkang Wang, et al. ∙ 0 ∙ share

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

Self-driving vehicles need to anticipate a diverse set of future traffic...
01/16/2021 ∙ by Alexander Cui, et al. ∙ 8 ∙ share

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Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting

In this paper, we address the important problem in self-driving of forec...
01/07/2021 ∙ by Katie Luo, et al. ∙ 19 ∙ share

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StrObe: Streaming Object Detection from LiDAR Packets

Many modern robotics systems employ LiDAR as their main sensing modality...
11/12/2020 ∙ by Davi Frossard, et al. ∙ 0 ∙ share

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

In this paper we propose a novel end-to-end learnable network that perfo...
08/13/2020 ∙ by Abbas Sadat, et al. ∙ 4 ∙ share

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RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

We tackle the problem of exploiting Radar for perception in the context ...
07/28/2020 ∙ by Bin Yang, et al. ∙ 6 ∙ share

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Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

In order to plan a safe maneuver an autonomous vehicle must accurately p...
07/23/2020 ∙ by Sergio Casas, et al. ∙ 5 ∙ share

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The Importance of Prior Knowledge in Precise Multimodal Prediction

Roads have well defined geometries, topologies, and traffic rules. While...
06/04/2020 ∙ by Sergio Casas, et al. ∙ 29 ∙ share

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PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

We tackle the problem of joint perception and motion forecasting in the ...
05/29/2020 ∙ by Ming Liang, et al. ∙ 10 ∙ share

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Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

In this paper, we tackle the problem of relational behavior forecasting ...
10/18/2019 ∙ by Sergio Casas, et al. ∙ 31 ∙ share

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Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction

Self-driving vehicles plan around both static and dynamic objects, apply...
10/17/2019 ∙ by Ajay Jain, et al. ∙ 21 ∙ share

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Sergio Casas

Featured Co-authors

Raquel Urtasun

Bin Yang

Ersin Yumer

Renjie Liao

Mengye Ren

Ming Liang

Rui Hu

Yun Chen

Yuwen Xiong

Wenyuan Zeng

Xinchen Yan

IntentNet: Learning to Predict Intention from Raw Sensor Data

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

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

End-to-end Interpretable Neural Motion Planner

TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

Diverse Complexity Measures for Dataset Curation in Self-driving

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

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

Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting

StrObe: Streaming Object Detection from LiDAR Packets

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

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

The Importance of Prior Knowledge in Precise Multimodal Prediction

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction