AffordDrive3D: Affordance-Aware World-Action Modeling with Spatial Understanding

arXiv:2610.11060 · cs.CV, cs.AI · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Affordance — 70× in this paper · explained in 1 episode
• World Model — 19× in this paper · explained in 7 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• International Conference on Computer Vision — 4× in this paper
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Extended Comfort — 2× in this paper
• GRPO — 2× in this paper · explained in 6 episodes
• Latent Reasoning — 2× in this paper · explained in 2 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adaptive Reasoning — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Drivable Area Compliance — 1× in this paper
• Ego Progress — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• History Comfort — 1× in this paper
• Lane Keeping — 1× in this paper
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• NAVSIM Driving Direction Compliance — 1× in this paper
• No At-Fault Collisions — 1× in this paper
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Traffic Light Compliance — 1× in this paper
• Transformer — 1× in this paper · explained in 2 episodes
• VLA Models — 1× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model
• DriveFuture: Future-Aware Latent World Models for Autonomous Driving
• Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
• Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
• Generalized Trajectory Scoring for End-to-end Multimodal Planning
• UniFuture: A 4D Driving World Model for Future Generation and Perception
• Depth Anything 3: Recovering the Visual Space from Any Views
• DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving
• GeoWAM: Visual Geometry World Action Models for Autonomous Driving
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• DriveWAM: Video Generative Priors Enable Scalable World-Action Modeling for Autonomous Driving
• Wan: Open and Advanced Large-Scale Video Generative Models
• Learning Vision-Language-Action World Models for Autonomous Driving
• Afford-VLA: Action-Aligned Visual Planning via Internalized Affordance
• WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving
• HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving
• AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding

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