V2X-WAM: A Cooperative World Action Model for End-to-End Autonomous Driving

arXiv:2609.37098 · cs.RO, cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-29

Updated: 2026-09-30

Terminology

• World Model — 13× in this paper · explained in 7 episodes
• Cooperation — 9× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Actor — 7× in this paper · explained in 3 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Quantization — 5× in this paper · explained in 12 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Communication Overhead — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Multimodal Reasoning — 3× in this paper · explained in 5 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Latent Reasoning — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Adaptive Reasoning — 1× in this paper · explained in 2 episodes
• Agent Interactions — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
• Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation
• OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving
• Learning Vision-Language-Action World Models for Autonomous Driving
• Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
• SimWAM: A Simple World Action Model for End-to-End Autonomous Driving

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving