World4Scorer: Outcome-Grounded World Modeling for Autonomous Driving

arXiv:2609.36438 · cs.RO · Submitted 2026-09-29 · Read on arXiv

cs.RO

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• World Model — 23× in this paper · explained in 7 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Transformer — 6× in this paper · explained in 2 episodes
• Regret — 5× in this paper · explained in 3 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Distillation — 2× in this paper · explained in 2 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Taxonomy — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning
• DriveFuture: Future-Aware Latent World Models for Autonomous Driving
• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
• SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse World
• Driving on Registers
• DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
• Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
• Generalized Trajectory Scoring for End-to-end Multimodal Planning
• The DAWN of World-Action Interactive Models
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
• GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving
• Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving
• DriveLaW:Unifying Planning and Video Generation in a Latent Driving World
• WAM-Flow: Parallel Coarse-to-Fine Motion Planning via Discrete Flow Matching for Autonomous Driving
• LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving
• Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning
• DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving