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Found 10 papers, 2 papers with code
MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion
We present MotionDiffuser, a diffusion based representation for the joint distribution of future trajectories over multiple agents.
CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships
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.
VN-Transformer: Rotation-Equivariant Attention for Vector Neurons
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.
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving
A whole-scene sparse input representation allows StopNet to scale to predicting trajectories for hundreds of road agents with reliable latency.
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving
We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving.
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
Scene Transformer: A unified architecture for predicting multiple agent trajectories
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
MODEC: Multimodal Decomposable Models for Human Pose Estimation
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.
