NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving
Vision-language models (VLMs) have emerged as a promising direction for end-to-end autonomous driving (AD) by jointly modeling visual observations, driving context, and language-based reasoning. However, existing VLM-based systems face a trade-off between high-level reasoning and motion planning: large models offer strong semantic understanding but are costly to adapt for precise control, whereas small VLM models can be fine-tuned efficiently but often exhibit weaker reasoning. We propose NaviDriveVLM, a decoupled framework that separates reasoning from action generation using a large-scale Navigator and a lightweight trainable Driver. This design preserves reasoning ability, reduces training cost, and provides an explicit interpretable intermediate representation for downstream planning. Experiments on the nuScenes benchmark show that NaviDriveVLM outperforms large VLM baselines in end-to-end motion planning.
69af9c69b3ab31dee1281dbd
Ximeng Tao
claimed_verified
2026-03-19T13:04:02.217Z
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Ximeng Tao
Ximeng0831
user
Ximeng0831
69af9c69b3ab31dee1281dbe
Pardis Taghavi
69af9c69b3ab31dee1281dbf
Dimitar Filev
69af9c69b3ab31dee1281dc0
Reza Langari
69af9c69b3ab31dee1281dc1
Gaurav Pandey
NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving
Vision-language models (VLMs) have emerged as a promising direction for end-to-end autonomous driving (AD) by jointly modeling visual observations, driving context, and language-based reasoning. However, existing VLM-based systems face a trade-off between high-level reasoning and motion planning: large models offer strong semantic understanding but are costly to adapt for precise control, whereas small VLM models can be fine-tuned efficiently but often exhibit weaker reasoning. We propose NaviDriveVLM, a decoupled framework that separates reasoning from action generation using a large-scale Navigator and a lightweight trainable Driver. This design preserves reasoning ability, reduces training cost, and provides an explicit interpretable intermediate representation for downstream planning. Experiments on the nuScenes benchmark show that NaviDriveVLM outperforms large VLM baselines in end-to-end motion planning.
NaviDriveVLM presents a decoupled vision-language model framework for autonomous driving that separates high-level reasoning from motion planning, achieving superior performance in end-to-end driving while reducing training costs.
vision-language models autonomous driving end-to-end motion planning large-scale Navigator lightweight trainable Driver decoupled framework semantic understanding motion planning