# IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving arXiv: https://arxiv.org/abs/2605.31476 ยท HTML: https://mlsanity.com/paper/2605.31476/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in World models for autonomous driving, one of 11 directions in World models. - Builds on 47 of its 85 references that are in the map. - First posted on arXiv 29 May 2026. ## Placed in - World models / [World models for autonomous driving](https://mlsanity.com/wm/world-models-for-autonomous-driving/) - End-to-end autonomous driving / [Generative simulation for driving](https://mlsanity.com/driving/generative-simulation-for-driving/) ## Builds on (most cited first, 30 of 47) - [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019) - [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023) - [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020) - [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023) - [VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning](https://mlsanity.com/paper/2402.13243/) (2024) - [Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation](https://mlsanity.com/paper/2406.06978/) (2024) - [EMMA: End-to-End Multimodal Model for Autonomous Driving](https://mlsanity.com/paper/2410.23262/) (2024) - [ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.08052/) (2025) - [Enhancing End-to-End Autonomous Driving with Latent World Model](https://mlsanity.com/paper/2406.08481/) (2024) - [FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving](https://mlsanity.com/paper/2505.17685/) (2025) - [Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation](https://mlsanity.com/paper/2412.15109/) (2024) - [Pseudo-Simulation for Autonomous Driving](https://mlsanity.com/paper/2506.04218/) (2025) - [DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving](https://mlsanity.com/paper/2510.12796/) (2025) - [Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation](https://mlsanity.com/paper/2503.12820/) (2025) - [Generalized Trajectory Scoring for End-to-end Multimodal Planning](https://mlsanity.com/paper/2506.06664/) (2025) - [DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving](https://mlsanity.com/paper/2505.19381/) (2025) - [AdaThinkDrive: Adaptive Thinking via Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2509.13769/) (2025) - [DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving](https://mlsanity.com/paper/2512.07745/) (2025) - [DriveLaW:Unifying Planning and Video Generation in a Latent Driving World](https://mlsanity.com/paper/2512.23421/) (2025) - [Navigation-Guided Sparse Scene Representation for End-to-End Autonomous Driving](https://mlsanity.com/paper/2409.18341/) (2024) - [ReSim: Reliable World Simulation for Autonomous Driving](https://mlsanity.com/paper/2506.09981/) (2025) - [iPad: Iterative Proposal-centric End-to-End Autonomous Driving](https://mlsanity.com/paper/2505.15111/) (2025) - [RAP: 3D Rasterization Augmented End-to-End Planning](https://mlsanity.com/paper/2510.04333/) (2025) - [SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving](https://mlsanity.com/paper/2601.05640/) (2026) - [SimScale: Learning to Drive via Real-World Simulation at Scale](https://mlsanity.com/paper/2511.23369/) (2025) - [IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model](https://mlsanity.com/paper/2508.06571/) (2025) - [ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving](https://mlsanity.com/paper/2508.11428/) (2025) - [Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving](https://mlsanity.com/paper/2509.20109/) (2025) - [LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving](https://mlsanity.com/paper/2512.20563/) (2025) - [Future-Aware End-to-End Driving: Bidirectional Modeling of Trajectory Planning and Scene Evolution](https://mlsanity.com/paper/2510.11092/) (2025) ## Built on by (most cited first, 2 of 2) - [DA-WAM: Decision-Aligned Future Latents for Driving World Models](https://mlsanity.com/paper/2608.19085/) (2026) - [MM-Future: Multi-Mode Joint World-Action Modeling for Autonomous Driving](https://mlsanity.com/paper/2609.20377/) (2026) ## Abstract (arXiv) End-to-end autonomous driving has emerged as a compelling paradigm for learning planning directly from sensor observations, while recent world-model-based approaches further enrich this paradigm by enabling explicit reasoning about how the scene may evolve in the future. Yet future prediction alone does not guarantee better planning unless the predicted evolution can be converted into planning-relevant trajectory updates. Many current methods still forecast future scene states without explicitly decoding the motion implications hidden in state transitions. As a result, future reasoning often remains descriptively useful but only weakly coupled to executable motion generation. To address this limitation, we propose IDOL, an inverse-dynamics-guided future prediction framework for world-model-based end-to-end planning in latent BEV space, where inverse dynamics serves as the key bridge between future prediction and trajectory optimization. IDOL first predicts multiple future latent scene states with a BEV world model, then applies an inverse dynamics model to adjacent latent futures to decode transition-aware trajectory features and recover planning-relevant motion deltas that explain how the latent world evolves over time. These transition features refine the candidate-specific planning queries through spatial cross-attention and global modulation. A lightweight closed-loop refinement module feeds the refined queries back into candidate-conditioned latent rollouts, enabling an additional round of future-informed query refinement before trajectory decoding. By introducing inverse dynamics into latent future reasoning, IDOL tightens the coupling between world modeling and planning. Extensive experiments on the NAVSIM v1 and NAVSIM v2 benchmarks show that IDOL achieves state-of-the-art performance among comparable methods.