DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving

• URL: http://arxiv.org/abs/2505.19239v1
• Date: Sun, 25 May 2025 17:27:59 GMT
• Title: DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving
• Authors: Chen Shi, Shaoshuai Shi, Kehua Sheng, Bo Zhang, Li Jiang,
• Abstract summary: We present DriveX, a self-supervised world model that learns general scene dynamics and holistic representations from driving videos.<n>DriveX introduces Omni Scene Modeling (OSM), a module that unifies multimodal supervision-3D point cloud forecasting, 2D semantic representation, and image generation.<n>For downstream adaptation, we design Future Spatial Attention (FSA), a unified paradigm that dynamically aggregates features from DriveX's predictions to enhance task-specific inference.
• Score: 20.197094443215963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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