arXiv AI
CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
5d ago
DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving
DriveWLA is a unified World‑Language‑Action model that integrates semantic understanding, future scene dynamics, and trajectory generation for autonomous driving. It employs a Mixture‑of‑Transformers architecture with three experts—World, Language, and Action—connected by Cross‑Expert Attention to align semantic and dynamic representations. After reinforcement learning refinement, DriveWLA outperforms existing VLM‑based and world‑model‑based planners on NAVSIM benchmarks and generalizes well to other datasets without fine‑tuning.
Hugging Face Trending Papers
Aug 13
BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving
Autonomous driving requires planning under both semantic constraints and predictive dynamics. Existing end-to-end driving approaches, however, typically emphasize only one side of this requirement: Vision-Language-Action (VLA) models exploit VLM priors for semantic reasoning, while World Action Models (WAMs) provide future-aware prediction through generative world modeling.
arXiv AI
Aug 14
BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving
arXiv:2608. 12854v1 Announce Type: cross Abstract: Autonomous driving requires planning under both semantic constraints and predictive dynamics.
arXiv AI
Jul 10
WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
arXiv:2607. 08375v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have advanced end-to-end autonomous driving.
arXiv AI
Jun 30
LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving
arXiv:2606. 29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning.
arXiv AI
Jul 7
AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
arXiv:2607. 03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories.