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.
https://arxiv.org/abs/2005.04259
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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.
MapATM improves lane divider AP by 4.6 and mAP by 2.6 on NuScenes by treating actor trajectories as structural priors for road geometry.
BEVPredFormer uses attention-based temporal processing and 3D camera projection to match or exceed prior methods on nuScenes for BEV instance prediction.
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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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.
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MapATM: Enhancing HD Map Construction through Actor Trajectory Modeling
MapATM improves lane divider AP by 4.6 and mAP by 2.6 on NuScenes by treating actor trajectories as structural priors for road geometry.
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BEVPredFormer: Spatio-temporal Attention for BEV Instance Prediction in Autonomous Driving
BEVPredFormer uses attention-based temporal processing and 3D camera projection to match or exceed prior methods on nuScenes for BEV instance prediction.