DriveFuture achieves SOTA results on NAVSIM by conditioning latent world model states on future predictions to directly inform trajectory planning.
The role of world models in shaping autonomous driving: A comprehensive survey
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Infrastructure-centric world models use roadside sensors' temporal depth to complement vehicle spatial breadth for better traffic simulation and prediction.
Re2Pix decomposes video prediction into semantic feature forecasting followed by representation-conditioned diffusion synthesis, with nested dropout and mixed supervision to handle prediction errors.
DriVerse is a generative model that simulates driving scenes from an image and trajectory using multimodal prompting and motion alignment, achieving better performance on nuScenes and Waymo datasets with minimal training.
citing papers explorer
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DriveFuture: Future-Aware Latent World Models for Autonomous Driving
DriveFuture achieves SOTA results on NAVSIM by conditioning latent world model states on future predictions to directly inform trajectory planning.
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Infrastructure-Centric World Models: Bridging Temporal Depth and Spatial Breadth for Roadside Perception
Infrastructure-centric world models use roadside sensors' temporal depth to complement vehicle spatial breadth for better traffic simulation and prediction.
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Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction
Re2Pix decomposes video prediction into semantic feature forecasting followed by representation-conditioned diffusion synthesis, with nested dropout and mixed supervision to handle prediction errors.
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DriVerse: Navigation World Model for Driving Simulation via Multimodal Trajectory Prompting and Motion Alignment
DriVerse is a generative model that simulates driving scenes from an image and trajectory using multimodal prompting and motion alignment, achieving better performance on nuScenes and Waymo datasets with minimal training.
- OpenWorldLib: A Unified Codebase and Definition of Advanced World Models