PlayGen-MoG uses a shared Mixture-of-Gaussians head across agents plus relative attention to generate diverse coordinated plays from a single static formation, achieving 1.68 yard ADE and 3.98 yard FDE with full mixture utilization on football data.
Scene transformer: A unified architecture for predicting mul- tiple agent trajectories
11 Pith papers cite this work. Polarity classification is still indexing.
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RiskFlow is a closed-loop multi-agent traffic generation framework that uses action-space transport with a JVP-based objective to produce realistic safety-critical scenarios faster than diffusion models.
EdgeVTP delivers the lowest measured end-to-end latency on Jetson-class platforms while matching or exceeding state-of-the-art accuracy on highway trajectory benchmarks by using bounded graph interactions and a one-shot curve decoder.
Closed-loop on-policy training with a reactive goal-oriented scene decoder cuts collision rates by up to 79.5% in dense traffic compared to standard open-loop baselines.
DeepFleet develops and compares four foundation model architectures for multi-agent robot fleet coordination using warehouse data, finding robot-centric and graph-floor models most promising for prediction and scaling.
VADv2 introduces a probabilistic planning model that discretizes the high-dimensional action space into tokens, interacts them with scene tokens to predict action distributions, and reports SOTA closed-loop results on CARLA Town05 and Bench2Drive.
Self-play RL regularized with 30 minutes of human data produces driving policies that coordinate with humans, training in 15 hours on one GPU with 2500x less data than imitation learning.
CaAD adds ego-centric joint-causal modeling and causality-aware policy alignment to end-to-end driving, reporting Driving Score 87.53 and PDMS 91.1 on Bench2Drive and NAVSIM.
An instance-centric representation with local frames, relative positional encodings, and adaptive reward transformation in adversarial IRL yields scalable, accurate, and robust behavior models for multi-agent driving simulation.
PS framework integrates MCTS with query-centric prediction to simulate and cost ego planning actions while accounting for interactive scenario responses on the Argoverse 2 dataset.
A conditional flow matching model generates realistic safety-critical traffic scenarios by turning nominal scenes into dangerous rollouts using combined simulation and real data.
citing papers explorer
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PlayGen-MoG: Framework for Diverse Multi-Agent Play Generation via Mixture-of-Gaussians Trajectory Prediction
PlayGen-MoG uses a shared Mixture-of-Gaussians head across agents plus relative attention to generate diverse coordinated plays from a single static formation, achieving 1.68 yard ADE and 3.98 yard FDE with full mixture utilization on football data.
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RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation
RiskFlow is a closed-loop multi-agent traffic generation framework that uses action-space transport with a JVP-based objective to produce realistic safety-critical scenarios faster than diffusion models.
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EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications
EdgeVTP delivers the lowest measured end-to-end latency on Jetson-class platforms while matching or exceeding state-of-the-art accuracy on highway trajectory benchmarks by using bounded graph interactions and a one-shot curve decoder.
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Goal-Oriented Reactive Simulation for Closed-Loop Trajectory Prediction
Closed-loop on-policy training with a reactive goal-oriented scene decoder cuts collision rates by up to 79.5% in dense traffic compared to standard open-loop baselines.
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DeepFleet: Multi-Agent Foundation Models for Mobile Robots
DeepFleet develops and compares four foundation model architectures for multi-agent robot fleet coordination using warehouse data, finding robot-centric and graph-floor models most promising for prediction and scaling.
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VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
VADv2 introduces a probabilistic planning model that discretizes the high-dimensional action space into tokens, interacts them with scene tokens to predict action distributions, and reports SOTA closed-loop results on CARLA Town05 and Bench2Drive.
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Human-like autonomy emerges from self-play and a pinch of human data
Self-play RL regularized with 30 minutes of human data produces driving policies that coordinate with humans, training in 15 hours on one GPU with 2500x less data than imitation learning.
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Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling
CaAD adds ego-centric joint-causal modeling and causality-aware policy alignment to end-to-end driving, reporting Driving Score 87.53 and PDMS 91.1 on Bench2Drive and NAVSIM.
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Toward Efficient and Robust Behavior Models for Multi-Agent Driving Simulation
An instance-centric representation with local frames, relative positional encodings, and adaptive reward transformation in adversarial IRL yields scalable, accurate, and robust behavior models for multi-agent driving simulation.
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Planning by Simulation: Motion Planning with Learning-based Parallel Scenario Prediction for Autonomous Driving
PS framework integrates MCTS with query-centric prediction to simulate and cost ego planning actions while accounting for interactive scenario responses on the Argoverse 2 dataset.
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Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
A conditional flow matching model generates realistic safety-critical traffic scenarios by turning nominal scenes into dangerous rollouts using combined simulation and real data.