IDOL uses inverse dynamics on adjacent predicted latent futures to extract planning-relevant motion deltas, then optimizes trajectories with a closed-loop refinement step, reporting SOTA results on NAVSIM v1 and v2.
arXiv preprint arXiv:2409.18341 (2024) 3, 4, 6, 10, 11
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Mosaic integrates rule-based and learned planners via arbitration graphs to set new state-of-the-art scores on nuPlan and interPlan benchmarks while cutting at-fault collisions by 30%.
PLAN-S decodes a style-conditioned four-channel semantic cost map from latent representations to bridge world models and planners in autonomous driving, reporting 0.55 m average L2 and 42% collision reduction on nuScenes plus PDMS gains on NAVSIM.
DynFlowDrive models action-conditioned scene transitions via rectified flow in latent space and adds stability-aware trajectory selection, showing gains on nuScenes and NavSim without added inference cost.
citing papers explorer
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IDOL: Inverse-Dynamics-Guided Future Prediction for End-to-End Autonomous Driving
IDOL uses inverse dynamics on adjacent predicted latent futures to extract planning-relevant motion deltas, then optimizes trajectories with a closed-loop refinement step, reporting SOTA results on NAVSIM v1 and v2.
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Mosaic: An Extensible Framework for Composing Rule-Based and Learned Motion Planners
Mosaic integrates rule-based and learned planners via arbitration graphs to set new state-of-the-art scores on nuPlan and interPlan benchmarks while cutting at-fault collisions by 30%.
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PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models
PLAN-S decodes a style-conditioned four-channel semantic cost map from latent representations to bridge world models and planners in autonomous driving, reporting 0.55 m average L2 and 42% collision reduction on nuScenes plus PDMS gains on NAVSIM.
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DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving
DynFlowDrive models action-conditioned scene transitions via rectified flow in latent space and adds stability-aware trajectory selection, showing gains on nuScenes and NavSim without added inference cost.