MomWorld: Momentum-Aware Latent World Model for Long-Horizon Autonomous Driving

arXiv:2609.33737 · cs.RO · Submitted 2026-09-27 · Read on arXiv

cs.RO

Submitted: 2026-09-27

Updated: 2026-09-30

Code: https://github.com/modaxiansheng/MomWorld

Terminology

• World Model — 19× in this paper · explained in 7 episodes
• Future World Memory — 13× in this paper
• Consistency — 12× in this paper · explained in 3 episodes
• Robustness — 10× in this paper · explained in 5 episodes
• Latent World Rollout — 9× in this paper
• Accuracy — 7× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Flow Matching — 7× in this paper · explained in 8 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Aggregation — 5× in this paper · explained in 1 episode
• Latent World Models — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Scene-Adaptive Momentum Dynamics — 4× in this paper
• Temporal consistency — 4× in this paper · explained in 2 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Horizon-Aware Residual Fusion — 3× in this paper
• Momentum-Guided Residual Flow — 3× in this paper
• Trajectory Prediction Consistency — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Reasoning — 1× in this paper · explained in 2 episodes
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
• PV-WM: A Heterogeneous Micro-Macro World Model for Articulated Pedestrian-Vehicle Co-Rollout
• Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout
• DriveFine: Refining-Augmented Masked Diffusion VLA for Precise and Robust Driving
• DriveFuture: Future-Aware Latent World Models for Autonomous Driving
• Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
• End-to-End Driving with Online Trajectory Evaluation via BEV World Model
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
• Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
• Generalized Trajectory Scoring for End-to-end Multimodal Planning
• Fully Unified Motion Planning for End-to-End Autonomous Driving
• DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving
• MindDrive: An All-in-One Framework Bridging World Models and Vision-Language Model for End-to-End Autonomous Driving
• SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving
• Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
• Challenger: Affordable Adversarial Driving Video Generation
• DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning
• DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba
• AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning
• SpanVLA: Learning from Negative-Recovery Samples with Fast Action Bridging for Vision-Language-Action Model

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