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JEPAhead model forecasts urban driving futures with 69% accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed JEPAhead, a model fine-tuned from V-JEPA1 for causal forecasting in urban driving scenarios. By utilizing contiguous past observations and group-level causal attention, JEPAhead can predict eight future token groups, representing sixteen frames, in a single pass. The model demonstrated strong performance on the CARLA Town12 dataset, correctly identifying future driving scenarios with 69.0% accuracy, significantly outperforming a persistence model and a predictor trained from scratch with fewer parameters. Semantic analysis of the model's predictions also revealed driving-relevant information. AI

IMPACT This research could advance the capabilities of autonomous driving systems by improving their ability to predict future scenarios.

RANK_REASON Publication of a research paper detailing a new model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

JEPAhead model forecasts urban driving futures with 69% accuracy

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Publication of a research paper detailing a new model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Daniel G. P. Petrini, Davi Pincinato, Fabio Grassiotto, Pedro L. F. Prado, Alexandre F. S. Osorio, Raphael V. Ramos, Felipe Pamboukian ·

    JEPAhead: From Inpainting to Causal Forecasting for Urban Driving

    arXiv:2611.08657v1 Announce Type: new Abstract: We fine-tune a pre-trained V-JEPA1 predictor from bidirectional latent inpainting to action-free causal forecasting while keeping the encoder frozen. Using contiguous past observations, horizon-aligned targets, and group-level causa…

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