VGGTWorld-VLA extends the VGGT-World model by adding intention-conditioned control to 3D world evolution for autonomous driving. It introduces an action–semantic conditioning mechanism that injects driving semantics and ego‑motion into the future‑token stream, allowing different future geometry predictions for the same observed scene under alternative actions. A geometry–language–action bridge further integrates historical geometry, VLA semantic features, and maneuver/trajectory representations to jointly condition future geometry prediction, achieving competitive performance on NAVSIM and demonstrating the benefits of semantic and action conditioning.
By Zhaoyang Liu, Kun Jiang, Ziying Song, Diange Yang
AffordDrive3D is a world-action model that jointly learns future action-relevant regions and spatial geometry for autonomous driving. It uses a VLM backbone to forecast drivable areas and collision-critical regions while predicting future geometry from RGB world-model latents. On the NAVSIM benchmark, it achieves state‑of‑the‑art performance with 91.3 PDMS and 89.9 EPDMS, showing the benefit of combining affordance prediction with geometry modeling for trajectory planning.
By Tianhui Cai, Xinglong Sun, Chao Fang, Zhenxin Li, Rui Song, Jose M. Alvarez, Yunxiang Mao, Jiaqi Ma, Langechuan Liu
arXiv:2610.07381v1 Announce Type: new
Abstract: Modeling 3D scene geometry and its evolution over time is essential for autonomous driving and robotics. A common paradigm is to use world models to pr...
By Mehrdad Noori, Guile Wu, Sam Hosseini, Dongfeng Bai
PhysWAM is a unified world-action model for autonomous driving that jointly denoises multiview video, metric depth, and ego motion using a flow‑matching transformer. It introduces Coupled Point Projection (CPP), a geometric constraint that aligns generated depth points with LiDAR data after applying the predicted SE(3) ego motion, thereby enforcing physical consistency. At inference, trajectory selection uses a simple label‑free consensus rule, and the model demonstrates strong planning performance, zero‑shot transfer to unseen environments, and accurate, temporally coherent depth and video predictions.
By Dhruv Parikh, Fengcheng Yu, Quankai Gao, Jiawei Yang, Junjie Ye, Maulik Bhatt, Thang Vu, Charles Ochoa, Rowan McAllister, Igor Vasiljevic, Rajgopal Kannan, Viktor Prasanna, Vitor Guizilini, Yue Wang
WALT introduces a method to align latent trajectories with pretrained driving world models, creating a compact generative trajectory space that preserves action-relevant semantics without altering the original model. The approach uses a dual-branch autoencoder to map raw waypoints into this latent space and transfers visual world knowledge into trajectory representations. Experiments on NAVSIM benchmarks show modest performance gains and a 30.5% reduction in planner FLOPs, indicating that maintaining world representations while extracting action-relevant information can improve trajectory planning efficiency.
By Mingkai Jia, Jiaxin Guo, Zhijian Shu, Jiawei Xu, Mingxiao Li, Jintao Cheng, Ping Tan, Wei Yin
Drive‑HWM introduces a hierarchical slow‑fast world modeling framework for autonomous driving. The slow model predicts multi‑step future representations, while the fast model jointly predicts the next frame and immediate action using a lightweight multimodal backbone and an autoregressive expert. Dynamic‑Aware Latents, learned through optical‑flow prediction, explicitly capture motion dynamics, and experiments on NAVSIM v1 and v2 show strong driving performance with validated ablation studies.
By Zhaoxin Fan, Tianbao Zhang, Wenjun Wu, Xiaofeng Wang, Yeying Jin, Jian Zhao, Zheng Zhu, Shuicheng Yan