Relationally Grounded Latent World Models for Autonomous Driving

arXiv:2609.24626 · cs.CV, cs.RO · Submitted 2026-09-21 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-21

Updated: 2026-09-21

Terminology

• Alignment — 30× in this paper · explained in 1 episode
• World Model — 23× in this paper · explained in 7 episodes
• Grounding — 8× in this paper · explained in 3 episodes
• Actor — 6× in this paper · explained in 3 episodes
• Latent World Models — 6× in this paper · explained in 2 episodes
• Semantic Alignment — 6× in this paper · explained in 3 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Transformer — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 1× in this paper
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
• LAD-Drive: Bridging Language and Trajectory with Action-Aware Diffusion Transformers
• GAIA-1: A Generative World Model for Autonomous Driving
• Orbis: Overcoming Challenges of Long-Horizon Prediction in Driving World Models
• DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving
• DriveFuture: Future-Aware Latent World Models for Autonomous Driving
• Latent Chain-of-Thought World Modeling for End-to-End Driving
• Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
• DLWM: Dual Latent World Models enable Holistic Gaussian-centric Pre-training in Autonomous Driving
• DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving
• LaST-VLA: Thinking in Latent Spatio-Temporal Space for Vision-Language-Action in Autonomous Driving
• Bridging Structure and Language: Graph-Based Visual Reasoning for Autonomous Road Understanding
• GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving
• GraphPilot: Grounded Scene Graph Conditioning for Language-Based Autonomous Driving
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models