LWDrive uses future-frame supervision on VLMs to create world-model features that a multi-layer Foresight Cascade Planner refines into final trajectories, reporting 92.0 on NAVSIM and 89.6 on NAVSIM-v2.
Driveworld-vla: Unified latent-space world modeling with vision-language-action for au- tonomous driving.ArXiv, abs/2602.06521
7 Pith papers cite this work. Polarity classification is still indexing.
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DriveFuture achieves SOTA results on NAVSIM by conditioning latent world model states on future predictions to directly inform trajectory planning.
OneVL achieves superior accuracy to explicit chain-of-thought reasoning at answer-only latency by supervising latent tokens with a visual world model decoder that predicts future frames.
SteinsGateDrive decouples LLM inference latency from vehicle control by pre-selecting alpha, beta, and gamma worldline futures that a runtime validates against safety contracts until abort conditions trigger.
LVDrive improves closed-loop driving on Bench2Drive by adding latent future scene prediction to VLA models via unified embedding space processing and two-stage trajectory decoding.
SpanVLA reduces action generation latency via flow-matching conditioned on history and improves robustness by training on negative-recovery samples with GRPO and a dedicated reasoning dataset.
IRR-Drive adds an adaptive multimodal reflection step (text intention plus predicted future BEV) that lets a VLA model self-correct its trajectory plan according to scene complexity and reports SOTA on NAVSIM.
citing papers explorer
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LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving
LWDrive uses future-frame supervision on VLMs to create world-model features that a multi-layer Foresight Cascade Planner refines into final trajectories, reporting 92.0 on NAVSIM and 89.6 on NAVSIM-v2.
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DriveFuture: Future-Aware Latent World Models for Autonomous Driving
DriveFuture achieves SOTA results on NAVSIM by conditioning latent world model states on future predictions to directly inform trajectory planning.
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Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation
OneVL achieves superior accuracy to explicit chain-of-thought reasoning at answer-only latency by supervising latent tokens with a visual world model decoder that predicts future frames.
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LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model
LVDrive improves closed-loop driving on Bench2Drive by adding latent future scene prediction to VLA models via unified embedding space processing and two-stage trajectory decoding.
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SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model
SpanVLA reduces action generation latency via flow-matching conditioned on history and improves robustness by training on negative-recovery samples with GRPO and a dedicated reasoning dataset.
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Intend, Reflect, Refine: An Adaptive Multimodal Reflection Framework for Autonomous Driving
IRR-Drive adds an adaptive multimodal reflection step (text intention plus predicted future BEV) that lets a VLA model self-correct its trajectory plan according to scene complexity and reports SOTA on NAVSIM.