# LeGo-Drive: Language-enhanced Goal-oriented Closed-Loop End-to-End Autonomous Driving

arXiv: https://arxiv.org/abs/2403.20116 · HTML: https://mlsanity.com/paper/2403.20116/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Driving VLMs and open-vocabulary detection, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 26 of its 30 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Driving VLMs and open-vocabulary detection](https://mlsanity.com/mllm/driving-vlms-and-open-vocabulary-detection/)
- End-to-end autonomous driving / [Reasoning VLA and VLM driving policies](https://mlsanity.com/driving/reasoning-vla-and-vlm-driving-policies/)

## Builds on (most cited first, 26 of 26)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [DriveLM: Driving with Graph Visual Question Answering](https://mlsanity.com/paper/2312.14150/) (2023)
- [Differentiable Convex Optimization Layers](https://mlsanity.com/paper/1910.12430/) (2019)
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning](https://mlsanity.com/paper/2207.07601/) (2022)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [LMDrive: Closed-Loop End-to-End Driving with Large Language Models](https://mlsanity.com/paper/2312.07488/) (2023)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [TransVG: End-to-End Visual Grounding with Transformers](https://mlsanity.com/paper/2104.08541/) (2021)
- [Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline](https://mlsanity.com/paper/2206.08129/) (2022)
- [Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving](https://mlsanity.com/paper/2310.01957/) (2023)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [DC3: A learning method for optimization with hard constraints](https://mlsanity.com/paper/2104.12225/) (2021)
- [Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations](https://mlsanity.com/paper/2008.05930/) (2020)
- [Talk2Car: Taking Control of Your Self-Driving Car](https://mlsanity.com/paper/1909.10838/) (2019)
- [Improving One-stage Visual Grounding by Recursive Sub-query Construction](https://mlsanity.com/paper/2008.01059/) (2020)
- [VLP: Vision Language Planning for Autonomous Driving](https://mlsanity.com/paper/2401.05577/) (2024)
- [A Real-Time Cross-modality Correlation Filtering Method for Referring Expression Comprehension](https://mlsanity.com/paper/1909.07072/) (2019)
- [Theseus: A Library for Differentiable Nonlinear Optimization](https://mlsanity.com/paper/2207.09442/) (2022)
- [Grounding Linguistic Commands to Navigable Regions](https://mlsanity.com/paper/2112.13031/) (2021)
- [End-to-End Learning of Behavioural Inputs for Autonomous Driving in Dense Traffic](https://mlsanity.com/paper/2310.14766/) (2023)

## Built on by (most cited first, 12 of 12)

- [A Survey on Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2506.24044/) (2025)
- [NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models](https://mlsanity.com/paper/2503.12772/) (2025)
- [PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving](https://mlsanity.com/paper/2507.17596/) (2025)
- [AD-H: Language-guided Autonomous Driving with Hierarchical Agents](https://mlsanity.com/paper/2406.03474/) (2024)
- [Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.11234/) (2025)
- [ObjectReact: Learning Object-Relative Control for Visual Navigation](https://mlsanity.com/paper/2509.09594/) (2025)
- [ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving](https://mlsanity.com/paper/2604.05378/) (2026)
- [Diffusion-FS: Multimodal Free-Space Prediction via Diffusion for Autonomous Driving](https://mlsanity.com/paper/2507.18763/) (2025)
- [CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving](https://mlsanity.com/paper/2607.02841/) (2026)
- [DIV-Nav: Open-Vocabulary Spatial Relationships for Multi-Object Navigation](https://mlsanity.com/paper/2510.16518/) (2025)
- [DeepIPCv3: Event-Aware Multi-Modal Sensor Fusion for Sudden Pedestrian Crossing Avoidance](https://mlsanity.com/paper/2606.01277/) (2026)
- [Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Driving](https://mlsanity.com/paper/2607.10438/) (2026)

## Abstract (arXiv)

Existing Vision-Language models (VLMs) estimate either long-term trajectory waypoints or a set of control actions as a reactive solution for closed-loop planning based on their rich scene comprehension. However, these estimations are coarse and are subjective to their "world understanding" which may generate sub-optimal decisions due to perception errors. In this paper, we introduce LeGo-Drive, which aims to address this issue by estimating a goal location based on the given language command as an intermediate representation in an end-to-end setting. The estimated goal might fall in a non-desirable region, like on top of a car for a parking-like command, leading to inadequate planning. Hence, we propose to train the architecture in an end-to-end manner, resulting in iterative refinement of both the goal and the trajectory collectively. We validate the effectiveness of our method through comprehensive experiments conducted in diverse simulated environments. We report significant improvements in standard autonomous driving metrics, with a goal reaching Success Rate of 81%. We further showcase the versatility of LeGo-Drive across different driving scenarios and linguistic inputs, underscoring its potential for practical deployment in autonomous vehicles and intelligent transportation systems.