Social-Mamba introduces a Cycle Mamba block and social triplet factorization to achieve state-of-the-art trajectory forecasting accuracy with linear-time social interaction modeling on five benchmarks.
Latent variable sequential set transformers for joint multi-agent motion prediction,
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Retrocausal transformer decomposes multi-agent motion forecasts into marginals and pairwise joints, models uncertainty with compressed exponentials, achieves strong Waymo results, generalizes to Argoverse 2 and V2X-Seq, and enables implicit instruction following from standard training.
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.
VERDI aligns perception, prediction, and planning outputs of end-to-end AD models with VLM-generated text features at training time to embed structured reasoning, yielding up to 11% better l2 distance and 10% higher non-collision rate in closed-loop tests.
citing papers explorer
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Social-Mamba: Socially-Aware Trajectory Forecasting with State-Space Models
Social-Mamba introduces a Cycle Mamba block and social triplet factorization to achieve state-of-the-art trajectory forecasting accuracy with linear-time social interaction modeling on five benchmarks.
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RetroMotion: Retrocausal Motion Forecasting Models are Instructable
Retrocausal transformer decomposes multi-agent motion forecasts into marginals and pairwise joints, models uncertainty with compressed exponentials, achieves strong Waymo results, generalizes to Argoverse 2 and V2X-Seq, and enables implicit instruction following from standard training.
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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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VERDI: VLM-Embedded Reasoning for Autonomous Driving
VERDI aligns perception, prediction, and planning outputs of end-to-end AD models with VLM-generated text features at training time to embed structured reasoning, yielding up to 11% better l2 distance and 10% higher non-collision rate in closed-loop tests.