AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving
- Published
- Source
- arXiv
- Paper number
- 1137
- Field
- Robotics
- arXiv ID
- 2609.34085
Key points
- AD-E2E-JEPA is an autonomous-driving world model that predicts future latent visual representations and selects trajectories close to a target image.
- A learnable projector with SIGReg regularization reduces the number of planning patches to one-sixteenth and the feature dimension to one-quarter.
- In an A100 experiment evaluating 256 candidate trajectories, planning takes 0.8 seconds per scene, compared with 91.8 seconds for DINO-WM and 101.0 seconds for JEPA-WM.
- Using the pretrained projector in a separate imitation-learning setup raises EPDMS from 80.2 with random initialization to 85.4, demonstrating the value of reusing its representations.
- Zero-shot evaluation supplies ground-truth future images as goals and does not directly optimize safety, so the results do not establish safety on real roads.
Paper links
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