arXiv (All)
open access
Drive-HWM: Hierarchical World Models for Dynamic-Latent Guided Autonomous Driving
World models offer a promising paradigm for autonomous driving by predicting how traffic scenes may evolve and using such predictions to support action generation. However, existing approaches either separate future prediction from action generation or jointly predict them at the same temporal scale
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ForeDrive: Foresight-Guided End-to-End Autonomous Driving with a Planning-Relevant Latent World Model#1063004
- READ: Learning Risk-Informed Fields for End-to-End Autonomous Driving#1000325
- AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving#1119158
- WALT: Learning World-Model-Aligned Latent Trajectories for Autonomous Driving#1104635
- IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving#1117442
Record · ID 926481
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive