DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving
DriveX employs Omni Scene Modeling to learn generalizable scene dynamics, reducing Chamfer distance by 6% over 3s prediction, and excels in multi-task autonomous driving benchmarks.
Key Findings
Methodology
DriveX integrates self-supervised training with the Omni Scene Modeling (OSM) module, combining 3D point cloud forecasting, 2D semantic prediction, and image generation to build spatially-aware latent representations. It employs a decoupled hierarchical approach: first learning robust scene representations via a world encoder, then predicting future states with a future decoder conditioned on ego-actions. Dynamic-aware ray sampling emphasizes motion regions. The Future Spatial Attention (FSA) mechanism dynamically fuses predicted features into downstream tasks, enabling multi-task generalization. Experiments on nuScenes and NAVSIM show a 6% reduction in Chamfer distance for 3s point cloud forecasting and SOTA results in occupancy and flow tasks.
Key Results
- DriveX reduces Chamfer distance to 0.55m at 3s, outperforming ViDAR (1.01m) and DriveWorld (0.83m).
- Maintains 83.6% success rate in end-to-end driving on NAVSIM, with negligible performance drop when model is frozen.
- Achieves over 5% improvements in occupancy prediction and flow estimation, demonstrating versatile multi-task capability.
Significance
This work advances autonomous scene understanding by unifying multimodal data into a spatially-aware latent space, enabling robust multi-task performance. It addresses the core challenge of generalizable scene dynamics modeling, crucial for safety and reliability in autonomous systems. The self-supervised approach reduces annotation dependency, facilitating scalable deployment. DriveX’s ability to accurately predict scene evolution enhances decision-making and planning, pushing autonomous driving closer to real-world readiness.
Technical Contribution
DriveX’s main innovations include the OSM module for multimodal fusion, a hierarchical decoupled modeling strategy for efficient motion capture, and the FSA mechanism for dynamic feature integration. These components enable high-fidelity, generalizable scene representations and multi-task adaptability, surpassing prior methods that focus solely on geometric or semantic predictions. The self-supervised training paradigm further enhances scalability and robustness.
Novelty
This is the first framework to unify multimodal scene understanding in a spatially-aware latent space specifically for autonomous driving, combining point cloud, semantic, and visual cues. The decoupled hierarchical approach and dynamic ray sampling introduce new ways to model sparse motion signals, filling gaps left by prior models that either focus narrowly on geometry or semantics, or lack temporal coherence.
Limitations
- Performance degrades under adverse weather or sensor occlusion due to limited training data in such conditions.
- High computational complexity limits real-time deployment on edge devices.
- Long-term prediction errors accumulate, especially for highly dynamic scenes, requiring more advanced temporal modeling.
Future Work
Future efforts will focus on enhancing robustness in extreme weather, optimizing model efficiency for real-time deployment, and extending long-term dynamic predictions. Incorporating additional sensor modalities and unsupervised domain adaptation could further improve generalization across diverse environments.
AI Executive Summary
DriveX introduces a novel approach to autonomous scene modeling by leveraging Omni Scene Modeling (OSM), which fuses multimodal data—point clouds, semantics, and images—within a spatially-aware latent space. This self-supervised framework captures comprehensive scene dynamics, enabling robust prediction of future states. Its hierarchical decoupled design separates scene representation learning from future state decoding, improving efficiency and motion modeling accuracy.
The core technical innovation, the FSA mechanism, dynamically integrates predicted features into various downstream tasks, such as occupancy prediction, flow estimation, and end-to-end driving. Extensive experiments on nuScenes and NAVSIM datasets demonstrate that DriveX reduces the Chamfer distance of 3s point cloud prediction by 6%, achieving a distance of 0.55m, outperforming existing methods like ViDAR and DriveWorld. In downstream tasks, it attains state-of-the-art results, confirming its versatility and generalization.
This work significantly advances autonomous scene understanding by unifying multimodal data in a common latent space, addressing the limitations of task-specific models. Its self-supervised training paradigm reduces reliance on manual annotations, making scalable deployment feasible. The ability to accurately model scene evolution enhances decision-making robustness, paving the way for safer autonomous systems.
Despite these strengths, challenges remain in handling extreme weather conditions, reducing computational costs, and improving long-term dynamic predictions. Future research will aim to optimize model efficiency, incorporate additional sensors, and extend temporal horizons. Overall, DriveX marks a substantial step toward universal, reliable world models for autonomous driving, with broad implications for both academia and industry.
Deep Analysis
Background
自主驾驶技术经过多年的发展，从最初的感知和路径规划逐步演变为深度学习驱动的多模态场景理解。代表性工作如PointNet、VoxelNet实现了点云和图像的融合，但在动态场景和长时序预测方面仍有限。近年来，场景预测和世界模型成为研究热点，ViDAR、DriveWorld等在点云预测和视频生成方面取得突破，但多模态融合不足，泛化能力有限。多任务场景理解的需求促使研究者探索空间感知潜在空间的构建，旨在实现场景的全局理解与动态预测的统一。
Core Problem
现有自主驾驶模型多为任务特定，难以实现多场景、多任务的泛用性。点云、语义和图像信息多为孤立处理，导致信息融合不足，模型在复杂环境和长时序预测中表现不佳。如何设计一个融合多模态信息、具有空间感知能力的通用世界模型，成为核心难题。该问题的解决对于提升自主系统的鲁棒性和安全性具有重要意义，但受限于数据、模型复杂度和训练策略。
Innovation
DriveX的创新点包括：1）引入OSM模块，融合点云、语义和图像信息，构建多模态空间表征，增强场景理解；2）采用解耦潜在建模策略，将场景表示学习与未来状态解码分离，提高运动建模效率；3）设计动态感知射线采样，优先关注运动变化区域，改善动态目标建模；4）提出FSA机制，动态融合预测特征到多任务推理中，实现模型多场景适应。这些创新突破了现有模型在多模态融合和动态建模上的瓶颈。
Methodology
- �� 利用多视角摄像头和激光雷达数据，训练世界编码器，将多模态信息编码为空间感知潜在空间。
- �� 通过OSM模块，结合点云重建、语义预测和图像生成，提升潜在空间的丰富性和泛化能力。
- �� 采用解耦策略，先训练场景表示，再利用未来解码器预测未来状态，结合流场和动态感知射线采样增强运动建模。
- �� 未来解码器基于动作条件，预测未来潜在特征，利用变形注意力和距离加权插值实现未来状态的连续预测。
- �� 设计FSA机制，将预测的潜在特征动态融合到多任务模型中，提升占用、流场和控制任务的性能。
Experiments
在nuScenes和NAVSIM两个公开数据集上进行评估。采用Chamfer距离、IoU、mAVE等指标衡量点云预测和场景理解性能。不同预测时间（0.5s至3s）进行对比，消融验证OSM、解耦策略和FSA贡献。超参数包括潜在空间维度、射线采样数和训练轮次。模型在保持较低计算成本的同时，显著优于对比模型，验证多模态融合和动态建模的有效性。
Results
DriveX在3秒未来点云预测中Chamfer距离为0.55m，优于ViDAR（1.01m）和DriveWorld（0.83m），提升显著。在占用预测和流场估计任务中，性能提升超过5%，多任务性能优越。模型在端到端驾驶中保持83.6%的成功率，验证其强泛化能力。消融实验显示，OSM、解耦策略和FSA每项都对性能提升起到关键作用。
Applications
DriveX可应用于自主驾驶中的场景感知、路径规划和决策制定。其空间感知潜在空间支持多任务融合，提升系统鲁棒性。未来结合边缘计算优化模型效率，实现实时部署。长远来看，该技术有望推动自主系统向更高的智能化和安全性发展，适应复杂多变的环境。
Limitations & Outlook
模型在极端天气和遮挡条件下表现尚不理想，因训练数据缺乏极端场景。高计算成本限制在边缘设备上的实时应用。长时序动态预测存在误差累积，需引入更复杂的时序建模机制。未来需优化模型结构和训练策略，以增强鲁棒性和实用性。
Plain Language Accessible to non-experts
想象你在一个大型工厂里工作，工厂里有许多不同的机器和人员在不断变化。你需要不断观察这些变化，提前知道哪些机器可能出故障，哪些人员需要帮助。传统的方法就像只关注某一台机器或某一类人员，效果有限。DriveX就像是一个聪明的工厂助手，它能同时观察所有机器和人员，理解它们的关系和动作，预测未来的变化。它用一种特殊的“地图”把所有信息都放在一起，这样无论工厂发生什么变化，它都能快速反应，帮你提前做好准备。这种方法让工厂的管理变得更智能、更安全，也更高效。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的游戏，你要控制一个角色在一个大城市里跑来跑去。这个游戏里有很多东西在变化，比如汽车、行人、交通灯。以前的游戏只能让你看到眼前的场景，不能预测未来会发生什么。而DriveX就像是一个超级聪明的朋友，它可以观察城市的每个角落，记住所有的变化，然后告诉你未来几秒会发生什么，比如一辆车快要拐弯或者行人要过马路。它用一种特殊的“脑袋”把所有信息都装在一起，帮助你做出更聪明的决策。这样，你就能更安全、更快地完成任务，甚至避免危险。这个朋友不仅能帮你玩游戏，还能帮自动驾驶汽车在真实世界里安全行驶！
Abstract
Data-driven learning has advanced autonomous driving, yet task-specific models struggle with out-of-distribution scenarios due to their narrow optimization objectives and reliance on costly annotated data. We present DriveX, a self-supervised world model that learns generalizable scene dynamics and holistic representations (geometric, semantic, and motion) from large-scale driving videos. DriveX introduces Omni Scene Modeling (OSM), a module that unifies multimodal supervision-3D point cloud forecasting, 2D semantic representation, and image generation-to capture comprehensive scene evolution. To simplify learning complex dynamics, we propose a decoupled latent world modeling strategy that separates world representation learning from future state decoding, augmented by dynamic-aware ray sampling to enhance motion modeling. For downstream adaptation, we design Future Spatial Attention (FSA), a unified paradigm that dynamically aggregates spatiotemporal features from DriveX's predictions to enhance task-specific inference. Extensive experiments demonstrate DriveX's effectiveness: it achieves significant improvements in 3D future point cloud prediction over prior work, while attaining state-of-the-art results on diverse tasks including occupancy prediction, flow estimation, and end-to-end driving. These results validate DriveX's capability as a general-purpose world model, paving the way for robust and unified autonomous driving frameworks.