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

Junwei You; Weizhe Tang; Can Wang; Yan Zhao; Jun Hua; Haotian Shi; Wei Zhang; Lin Wang

cs.ROSep 29, 2026

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

Authors: Junwei You, Weizhe Tang, Can Wang, Yan Zhao, Jun Hua, Haotian Shi, Wei Zhang, Lin Wang, +1 more

Organizations: ITS Center, Research Institute of Highway Ministry of Transport, Beijing, 100088, China · State Key Lab of Intelligent Transportation System, Research Institute of Highway Ministry of Transport, Beijing, 100088, China · Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, WI, 53706, USA · Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, 430063, China · Engineering Research Center of Transportation Information and Safety, Ministry of Education, Wuhan, 430063, China · School of Transportation, Inner Mongolia University, Hohhot, 010021, China · College of Transportation, Tongji University, Shanghai, 201804, China

Abstract

Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providing valuable support for end-to-end autonomous driving. However, existing cooperative driving methods mainly exploit roadside information to enhance the representation of the current scene, while the future consequences of prospective driving actions are rarely modeled explicitly. This limits the ability of the planner to anticipate how its decisions may interact with the evolving traffic environment. To address this issue, we propose V2X-WAM, a cooperative world action model that tightly couples cooperative scene understanding, action generation, and future-world reasoning. V2X-WAM constructs a reliability-aware spatiotemporal representation from vehicle- and infrastructure-side observations, while compressing infrastructure information into a compact quantized message for efficient communication. Based on the resulting cooperative representation, a multimodal planner generates prospective trajectories, which explicitly condition future occupancy and dynamic-flow prediction. The predicted world consequences are then fed back to refine the planned trajectory, forming a closed interaction between action and future-world evolution. Experiments on a large-scale real-world cooperative driving dataset demonstrate that V2X-WAM consistently improves planning accuracy and safety over representative end-to-end cooperative driving methods, while achieving stronger future-world prediction and substantially lower communication overhead. Ablation studies further validate the effectiveness of the proposed design.

Figures & tables

Figure 1: Evolution of end-to-end cooperative autonomous driving paradigms.

Figure 2: Overall architecture of the proposed V2X-WAM.

Figure 3: Action-conditioned future-world modeling with joint occupancy–flow prediction and flow-guided occupancy transport.

Objective	Hyperparameter	Value
Planning	$\lambda_{\mathrm{best}}$	1.00
	$\lambda_{\mathrm{top}}$	1.25
	$\lambda_{\mathrm{mode}}$	0.35
	$\lambda_{\mathrm{act}}$	0.15
	$\lambda_{\mathrm{tail}}$	0.70
	$\lambda_{\mathrm{prog}}$	0.30

Table 1: Loss hyperparameters used for training V2X-WAM.

Method	L2 Error (m) $\downarrow$				Collision Rate (%) $\downarrow$				Trans. Cost (B/s) $\downarrow$
Method	1s	2s	3s	Avg.	1s	2s	3s	Avg.	Trans. Cost (B/s) $\downarrow$
VAD ∗ ( Jiang et al., 2023 )	1.65	2.72	3.80	2.72	0.86	1.21	1.28	1.12	–
UniAD ∗ ( Hu et al., 2023 )	1.26	2.22	3.06	2.18	0.88	1.18	1.32	1.13	–
SparseDrive ∗ ( Sun et al., 2025 )	1.02	1.69	2.37	1.69	0.46	1.23	1.28	0.99	–
Vanilla	1.36	2.29	3.32	2.32	1.03	0.88	1.32	1.08	$8.19\times 10^{7}$
V2VNet ( Wang et al., 2020 )	1.96	2.37	3.41	2.58	0.74	0.88	1.03	0.88	$8.19\times 10^{7}$

Table 2: End-to-end planning performance and transmission cost on V2X-Seq-SPD.

Method	Occupied IoU (%) $\uparrow$				Dynamic Flow EPE (m) $\downarrow$
Method	1s	2s	3s	Avg.	1s	2s	3s	Avg.
FIERY ( Hu et al., 2021 )	21.27	17.04	15.13	17.81	1.22	1.33	1.46	1.33
PowerBEV ( Li et al., 2023 )	23.46	18.85	18.32	20.21	1.03	1.14	1.20	1.12
StreamingFlow ( Shi et al., 2024 )	24.49	19.79	17.54	20.61	0.93	1.07	1.18	1.06
V2X-WAM (Ours)	27.90	21.69	19.63	23.07	1.01	1.09	1.16	1.08

Table 3: Future-world prediction performance on V2X-Seq-SPD.

Method	Avg. L2 Error (m) $\downarrow$	Avg. Collision (%) $\downarrow$	Avg. Occupied IoU (%) $\uparrow$	Avg. Flow EPE (m) $\downarrow$
Ego Only	1.11	0.05	19.80	1.15
w/o Action Conditioning	1.15	0.02	23.02	1.09
w/o Flow Transport	1.02	0.24	22.40	1.10
w/o Reliability	1.22	0.10	22.15	1.15
w/o Temporal Modeling	1.10	0.08	20.58	1.46
w/o World–Action Coupling	1.08	0.02	22.82	1.14

Table 4: Ablation study of V2X-WAM.

Figure 4: Qualitative planning and 3-s future-occupancy results for representative left-turn, straight, and right-turn scenes.

Figure 5: Controlled infrastructure-message analysis of future occupancy along road-aligned space–time slices.

Explore similar work

OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving
Jun 19, 2026Juntong Peng, Juanwu Lu, Yupeng Zhou +3V2X Causality-Aware End-To-End Autonomous Driving
College of Engineering, Purdue University, West Lafayette, IN 47907, USA
Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving
Jul 22, 2026Nuoran Li, Zhang Zhang, Yueran Zhao +2V2X Causality-Aware End-To-End Autonomous Driving
Shenzhen Automotive Research Institute National Engineering Research Center of Electric Vehicles Beijing Institute of Technology Shenzhen, China
SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving
Sep 3, 2026Jinyang Wang, Shiwei Li, Junjian Wang +12Efficient World-Action Model Autonomous Driving
Institute of Automation, Chinese Academy of Sciences · 2Chongqing Changan Technology Co., Ltd. · 3Civil Aviation University of China +2

Jun 19, 2026, cs.ROJ/K move · Enter open · S save

OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving

Juntong Peng, Juanwu Lu, Yupeng Zhou, Can Cui+2

College of Engineering, Purdue University, West Lafayette, IN 47907, USA

V2X Causality-Aware End-To-End Autonomous Driving Wireless Foundation Models Omnichem

Open paper AskPDF arXiv

We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving. The model directly interprets independent context sequences comprising multi-modal and multi-agent observations. The new design mitigates the computational cost of dense 3D perception, the vulnerability to data scarcity in cooperative scenarios, and the poor compliance with standardized messaging in existing methods that fuse multi-modal inputs into a shared representation. For training, we present an end-to-end supervised pipeline using a downstream trajectory generation loss, in which a high-capacity generative sequence planner implicitly learns to steer the model and leverage multi-modal inputs via cross-attention injection. As a foundation model, we demonstrate that OmniV2X pre-trained on large-scale single-agent planning datasets can efficiently adapt to cooperative environments by integrating the conditioning context with lightweight, standard-compliant V2X tokens. Evaluated on the DAIR-V2X-Seq dataset, OmniV2X outperforms existing end-to-end cooperative driving baselines, achieving state-of-the-art performance with less than 10% of the fine-tune V2X dataset and less than 1% of the communication bandwidth. We conduct comprehensive evaluations to demonstrate its computational efficiency and robustness under real-world constraints.

Abstract

Explore similar work

OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving

Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving

SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving