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.
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A new VLA model called SI uses a four-step chain-of-thought to derive driving intent and applies it via classifier-free guidance to a flow-matching trajectory generator, showing competitive Waymo scores and intent-controllable plans.
Co-training an SDC and pedestrians with MAPPO yields 78% goal success and 14% collisions versus 35%/33% for rule-based baselines, with jaywalking causing 62% of collisions and evidence of poor anticipation via speed differentials.
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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Action Emergence from Streaming Intent
A new VLA model called SI uses a four-step chain-of-thought to derive driving intent and applies it via classifier-free guidance to a flow-matching trajectory generator, showing competitive Waymo scores and intent-controllable plans.
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Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
Co-training an SDC and pedestrians with MAPPO yields 78% goal success and 14% collisions versus 35%/33% for rule-based baselines, with jaywalking causing 62% of collisions and evidence of poor anticipation via speed differentials.