mlsanityData to 7 Oct 2026

NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving

arXiv 2603.07901 ↗First posted 9 Mar 2026cs.RO cs.LGcited by 4

On the map: LLM reasoning → Chain-of-thought reasoning for driving

also in 2 more
Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning. Builds on 34 of its 36 references that are in the map. First posted on arXiv 9 Mar 2026.

Builds on

34 of 36 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail30 Oct
  2. 2025VERDI: VLM-Embedded Reasoning for Autonomous Driving21 May
  3. 2025DSDrive: Distilling Large Language Model for Lightweight End-to-End Autonomous Driving with Unified Reasoning and Planning8 May
  4. 2025LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking14 Jan
  5. 2024VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision19 Dec
  6. 2023Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving6 Dec

Other references · 28

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2025Qwen3 Technical Reportcited by 8,195
  3. 2023Visual Instruction Tuningcited by 7,792
  4. 2022Flow Matching for Generative Modelingcited by 5,426
  5. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  6. 2024π0: A Vision-Language-Action Flow Model for General Robot Controlcited by 2,297
  7. 2022Planning-oriented Autonomous Drivingcited by 1,068
  8. 2023VAD: Vectorized Scene Representation for Efficient Autonomous Drivingcited by 613
  9. 2023DriveLM: Driving with Graph Visual Question Answeringcited by 527
  10. 2024DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Modelscited by 482
  11. 2022ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learningcited by 365
  12. 2023GPT-Driver: Learning to Drive with GPTcited by 344
16 more
  1. 2023LMDrive: Closed-Loop End-to-End Driving with Large Language Modelscited by 301
  2. 2024Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillationcited by 244
  3. 2023Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?cited by 242
  4. 2023DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Drivingcited by 233
  5. 2023Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Drivingcited by 231
  6. 2024EMMA: End-to-End Multimodal Model for Autonomous Drivingcited by 226
  7. 2024GenAD: Generative End-to-End Autonomous Drivingcited by 225
  8. 2023Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenescited by 191
  9. 2025OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Modelcited by 172
  10. 2023A Language Agent for Autonomous Drivingcited by 161
  11. 2024OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Drivingcited by 93
  12. 2024RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Modelcited by 91
  13. 2025DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Drivingcited by 85
  14. 2025Distilling Multi-modal Large Language Models for Autonomous Drivingcited by 46
  15. 2024DriveGPT: Scaling Autoregressive Behavior Models for Drivingcited by 29
  16. 2024SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Imagescited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAscited by 1
  2. 2026Training a Student Expert via Semi-Supervised Foundation Model Distillationcited by 0
  3. 2026Beyond Textual Chain-of-Thought: A Survey on Action-Grounded Reasoning in Autonomous Drivingcited by 0
  4. 2026ActionSplice: In-Flight Action Editing for Interactive World Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning
  2. 2025VLMPlanner: Integrating Visual Language Models with Motion Planning
  3. 2025VLAD: A VLM-Augmented Autonomous Driving Framework with Hierarchical Planning and Interpretable Decision Process
  4. 2025ColaVLA: Leveraging Cognitive Latent Reasoning for Hierarchical Parallel Trajectory Planning in Autonomous Driving
  5. 2025dVLM-AD: Enhance Diffusion Vision-Language-Model for Driving via Controllable Reasoning
  6. 2025AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning
  7. 2025AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning
  8. 2025LLaViDA: A Large Language Vision Driving Assistant for Explicit Reasoning and Enhanced Trajectory Planning