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ADriver-I: A General World Model for Autonomous Driving

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arxiv 2311.13549 v1 pith:O4WWUKRO submitted 2023-11-22 cs.CV cs.RO

Fan Jia , Weixin Mao , Yingfei Liu , Yucheng Zhao , Yuqing Wen , Chi Zhang , Xiangyu Zhang , Tiancai Wang This is my paper
classification cs.CVcs.RO
keywords adriver-iautonomouscontroldrivingvision-actionmodelpairsworld
verification ladder T0 review T1 audit T2 compute T3 formal
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Typically, autonomous driving adopts a modular design, which divides the full stack into perception, prediction, planning and control parts. Though interpretable, such modular design tends to introduce a substantial amount of redundancy. Recently, multimodal large language models (MLLM) and diffusion techniques have demonstrated their superior performance on comprehension and generation ability. In this paper, we first introduce the concept of interleaved vision-action pair, which unifies the format of visual features and control signals. Based on the vision-action pairs, we construct a general world model based on MLLM and diffusion model for autonomous driving, termed ADriver-I. It takes the vision-action pairs as inputs and autoregressively predicts the control signal of the current frame. The generated control signals together with the historical vision-action pairs are further conditioned to predict the future frames. With the predicted next frame, ADriver-I performs further control signal prediction. Such a process can be repeated infinite times, ADriver-I achieves autonomous driving in the world created by itself. Extensive experiments are conducted on nuScenes and our large-scale private datasets. ADriver-I shows impressive performance compared to several constructed baselines. We hope our ADriver-I can provide some new insights for future autonomous driving and embodied intelligence.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HyWorldVLA: A Vision-Language-Action Model with Hybrid World Modeling for Autonomous Driving

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A hybrid world model that combines pixel-token prediction with latent prediction beats both pixel-only and latent-only world models on NAVSIM and is more robust to scene noise.

  2. Targeted Structure Completion for Sparse-View 3D Reconstruction in Autonomous Driving

    cs.CV 2026-07 conditional novelty 6.0 of 10

    FocusGS localizes a 3D geometric ambiguity manifold from depth discontinuities and instantiates continuous Gaussian queries only there, yielding SOTA sparse-view driving reconstruction with far fewer Gaussians.

  3. AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models

    cs.RO 2026-03 conditional novelty 6.0 of 10

    AutoWorld learns a self-supervised LiDAR occupancy world model and conditions a diffusion-based motion generator on its forecasts, reporting the top Waymo Sim Agents realism score.

  4. WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World

    cs.CV 2025-12 conditional novelty 6.0 of 10

    A five-aspect, 24-metric benchmark, a 26K human-annotated dataset, and an AI evaluator show that today's driving world models cannot simultaneously look real, respect geometry, and behave safely.

  5. ProphetDWM: A Driving World Model for Rolling Out Future Actions and Videos

    cs.CV 2025-05 conditional novelty 6.0 of 10

    ProphetDWM is a one-stage diffusion world model that jointly predicts future driving video and low-level actions from a current frame and a short action sequence.

  6. 3D and 4D World Modeling: A Survey

    cs.CV 2025-09 conditional novelty 5.0 of 10

    A survey that defines 3D/4D world modeling, organizes methods into VideoGen, OccGen, and LiDARGen categories, and compiles datasets, metrics, and benchmark numbers.

  7. Seeing Clearly, Forgetting Deeply: Revisiting Fine-Tuned Video Generators for Driving Simulation

    cs.CV 2025-08 conditional novelty 5.0 of 10

    Fine-tuning video generators on driving data can improve visual fidelity while degrading how accurately the model predicts the movement of cars and pedestrians.

  8. LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model

    cs.CV 2025-06 reject novelty 5.0 of 10

    A hierarchical coarse-to-fine diffusion transformer with cross-granularity distillation improves long-term driving video prediction, but the reported gains may be inflated by future-derived text prompts and a selected...

  9. A Survey on Vision-Language-Action Models for Autonomous Driving

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A survey organizes vision-language-action models for autonomous driving into four stages, compares over 20 systems, and catalogs datasets, benchmarks, and open challenges.

  10. AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software

    cs.RO 2025-05 conditional novelty 4.0 of 10

    AWML is a new MLOps integration that connects MMDetection/MMDetection3D models to Autoware/ROS 2 and couples deployment with pseudo-label active learning, demonstrated on private taxi and bus datasets.

  11. A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach

    cs.RO 2025-12 conditional novelty 3.0 of 10

    A position/review paper argues data-driven model predictive control is the best route to safe, adaptive, human-like autonomous-driving motion planning, but provides no new derivation or experiment.

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