IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving

arXiv:2605.31476 · cs.RO · Submitted 2026-05-29 · Read on arXiv

cs.RO

Submitted: 2026-05-29

Updated: 2026-09-26

Comments: 23 pages, 5 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 33× in this paper · explained in 7 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Benchmark — 12× in this paper · explained in 2 episodes
• Decode — 8× in this paper · explained in 2 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Flow Matching — 4× in this paper · explained in 7 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Distillation — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Predictive Driver Model Score — 3× in this paper
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Actionability — 1× in this paper · explained in 3 episodes
• Active Perception — 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
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Extended Predictive Driver Model Score — 1× in this paper
• Fidelity — 1× in this paper · explained in 2 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• Pseudo-Simulation for Autonomous Driving
• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
• RAP: 3D Rasterization Augmented End-to-End Planning
• iPad: Iterative Proposal-centric End-to-End Autonomous Driving
• FlowAD: Ego-Scene Interactive Modeling for Autonomous Driving
• Dream to Control: Learning Behaviors by Latent Imagination
• Mastering Atari with Discrete World Models
• Mastering Diverse Domains through World Models
• GAIA-1: A Generative World Model for Autonomous Driving
• EMMA: End-to-End Multimodal Model for Autonomous Driving
• DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving
• IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model
• WPT: World-to-Policy Transfer via Online World Model Distillation
• SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse World
• ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving
• SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving
• Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
• Navigation-Guided Sparse Scene Representation for End-to-End Autonomous Driving
• Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving
• Enhancing End-to-End Autonomous Driving with Latent World Model

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