Search Results for author: Ben Sapp

Found 10 papers, 2 papers with code

CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships

1 code implementation7 Jul 2022 Rebecca Roelofs, Liting Sun, Ben Caine, Khaled S. Refaat, Ben Sapp, Scott Ettinger, Wei Chai

Finally, we release the causal agent labels (at https://github. com/google-research/causal-agents) as an additional attribute to WOMD and the robustness benchmarks to aid the community in building more reliable and safe deep-learning models for motion forecasting.

Attribute Autonomous Vehicles +1

VN-Transformer: Rotation-Equivariant Attention for Vector Neurons

no code implementations8 Jun 2022 Serge Assaad, Carlton Downey, Rami Al-Rfou, Nigamaa Nayakanti, Ben Sapp

Rotation equivariance is a desirable property in many practical applications such as motion forecasting and 3D perception, where it can offer benefits like sample efficiency, better generalization, and robustness to input perturbations.

3D Shape Classification Motion Forecasting

StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving

no code implementations2 Jun 2022 Jinkyu Kim, Reza Mahjourian, Scott Ettinger, Mayank Bansal, Brandyn White, Ben Sapp, Dragomir Anguelov

A whole-scene sparse input representation allows StopNet to scale to predicting trajectories for hundreds of road agents with reliable latency.

Motion Forecasting

Occupancy Flow Fields for Motion Forecasting in Autonomous Driving

no code implementations8 Mar 2022 Reza Mahjourian, Jinkyu Kim, Yuning Chai, Mingxing Tan, Ben Sapp, Dragomir Anguelov

We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving.

Motion Estimation Motion Forecasting

MODEC: Multimodal Decomposable Models for Human Pose Estimation

no code implementations CVPR 2013 Ben Sapp, Ben Taskar

Unlike other multimodal models, our approach includes both global and local pose cues and uses a convex objective and joint training for mode selection and pose estimation.

Pose Estimation

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