PedestrianQA is a new benchmark that turns pedestrian behavior prediction into VLM question-answering with rationales, reporting improved intention classification, trajectory accuracy, and explanation quality after fine-tuning on multiple existing video datasets.
Lg-traj: Llm guided pedestrian trajectory prediction
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MoveFM-R is a framework that bridges mobility foundation models and LLMs using semantically enhanced location encoding, progressive curriculum alignment, and interactive self-reflection to generate plausible trajectories from language inputs.
Encore improves trajectory prediction by deriving explicitly biased rehearsal trajectories from ego observations to condition forecasts and simulate agent subjectivities.
citing papers explorer
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PEDESTRIANQA: A Benchmark for Vision-Language Models on Pedestrian Intention and Trajectory Prediction
PedestrianQA is a new benchmark that turns pedestrian behavior prediction into VLM question-answering with rationales, reporting improved intention classification, trajectory accuracy, and explanation quality after fine-tuning on multiple existing video datasets.
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MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning
MoveFM-R is a framework that bridges mobility foundation models and LLMs using semantically enhanced location encoding, progressive curriculum alignment, and interactive self-reflection to generate plausible trajectories from language inputs.
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Encore: Conditioning Trajectory Forecasting via Biased Ego Rehearsals
Encore improves trajectory prediction by deriving explicitly biased rehearsal trajectories from ego observations to condition forecasts and simulate agent subjectivities.