Enactor is an actor-centric generative transformer model with spatial-temporal attention for closed-loop microsimulation of vehicle trajectories at signalized intersections, outperforming baselines on SUMO distribution matching and real-world prediction tasks.
Motion transformer with global intention localization and local movement refinement
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
MAPLE proposes latent multi-agent rollouts with supervised fine-tuning followed by reinforcement learning using safety, progress, interaction, and diversity rewards to enable scalable closed-loop training for end-to-end autonomous driving.
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.
A differentiable motion forecasting model retrieves and refines interpretable trajectory anchors from a contrastively learned motion bank to improve transparency without sacrificing multi-modal accuracy.
citing papers explorer
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A Generative Model for Closed-Loop Microsimulation of Signalized Intersections
Enactor is an actor-centric generative transformer model with spatial-temporal attention for closed-loop microsimulation of vehicle trajectories at signalized intersections, outperforming baselines on SUMO distribution matching and real-world prediction tasks.
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MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving
MAPLE proposes latent multi-agent rollouts with supervised fine-tuning followed by reinforcement learning using safety, progress, interaction, and diversity rewards to enable scalable closed-loop training for end-to-end autonomous driving.
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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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Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank
A differentiable motion forecasting model retrieves and refines interpretable trajectory anchors from a contrastively learned motion bank to improve transparency without sacrificing multi-modal accuracy.