DRIVE-CHOREO uses three LLM agents to create a unified position-aware token sequence co-compressed with multi-view video, achieving SOTA BEV mAP of 21.6 and +2.4 NDS improvement on nuScenes.
Unleashing generalization of end-to-end autonomous driving with controllable long video generation
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
AutoScale is a closed-loop data engine using Graph-RAE for scene representation and Cluster-GA for importance-based retrieval to improve real-synthetic co-training for autonomous driving.
HERMES++ unifies 3D scene understanding and future geometry prediction in driving scenes via BEV representations, LLM-enhanced queries, a temporal link, and joint geometric optimization.
ProDrive couples a query-centric planner with a BEV world model for end-to-end ego-environment co-evolution, enabling future-outcome assessment that improves safety and efficiency over reactive baselines on NAVSIM v1.
DriveLaW unifies video world modeling and trajectory planning by injecting video-generator latents into a diffusion planner, achieving SOTA video prediction and a new record on the NAVSIM planning benchmark.
GAIA-2 is a controllable latent diffusion world model that produces spatiotemporally consistent multi-view videos for autonomous driving simulation across diverse geographies.
citing papers explorer
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OmniDrive: An LLM-Choreographed Multi-Agent World Model with Unified Latent Co-Compression for Multi-View Driving Video Generation
DRIVE-CHOREO uses three LLM agents to create a unified position-aware token sequence co-compressed with multi-view video, achieving SOTA BEV mAP of 21.6 and +2.4 NDS improvement on nuScenes.
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Closed Loop Dynamic Driving Data Mixture for Real-Synthetic Co-Training
AutoScale is a closed-loop data engine using Graph-RAE for scene representation and Cluster-GA for importance-based retrieval to improve real-synthetic co-training for autonomous driving.
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HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation
HERMES++ unifies 3D scene understanding and future geometry prediction in driving scenes via BEV representations, LLM-enhanced queries, a temporal link, and joint geometric optimization.
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ProDrive: Proactive Planning for Autonomous Driving via Ego-Environment Co-Evolution
ProDrive couples a query-centric planner with a BEV world model for end-to-end ego-environment co-evolution, enabling future-outcome assessment that improves safety and efficiency over reactive baselines on NAVSIM v1.
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DriveLaW:Unifying Planning and Video Generation in a Latent Driving World
DriveLaW unifies video world modeling and trajectory planning by injecting video-generator latents into a diffusion planner, achieving SOTA video prediction and a new record on the NAVSIM planning benchmark.
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GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving
GAIA-2 is a controllable latent diffusion world model that produces spatiotemporally consistent multi-view videos for autonomous driving simulation across diverse geographies.
- InfiniVerse: Occupancy Guided Unbounded Scene Generation for Autonomous Driving
- Optimization-Guided Diffusion for Interactive Scene Generation