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arXiv AI By Shuzheng Wang, Haoxuan Li, Shengzhen Li, Chenxi Wu, Yajing Sun, Lu Hou, Wenyu Liu, Xinggang Wang

DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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