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Found 8 papers, 4 papers with code
MotionLM: Multi-Agent Motion Forecasting as Language Modeling
Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain.
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints
Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas.
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
In this paper, we present Wayformer, a family of attention based architectures for motion forecasting that are simple and homogeneous.
Ranked #6 on
Motion Forecasting
on Argoverse CVPR 2020
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.
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction
Predicting the future behavior of road users is one of the most challenging and important problems in autonomous driving.
Ranked #16 on
Motion Forecasting
on Argoverse CVPR 2020
Agent Prioritization for Autonomous Navigation
We propose a system to rank agents around an autonomous vehicle (AV) in real time.
Decomposing Parameter Estimation Problems
We propose a technique for decomposing the parameter learning problem in Bayesian networks into independent learning problems.
EDML for Learning Parameters in Directed and Undirected Graphical Models
Second, it facilitates the design of EDML algorithms for new graphical models, leading to a new algorithm for learning parameters in Markov networks.
