# Large Language Model-assisted Autonomous Vehicle Recovery from Immobilization

arXiv: https://arxiv.org/abs/2510.26023 · HTML: https://mlsanity.com/paper/2510.26023/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning VLA and VLM driving policies, one of 9 directions in End-to-end autonomous driving.
- Builds on 31 of its 40 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Placed in

- 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, 30 of 31)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023)
- [Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving](https://mlsanity.com/paper/2406.03877/) (2024)
- [DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving](https://mlsanity.com/paper/2312.09245/) (2023)
- [Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://mlsanity.com/paper/2305.14078/) (2023)
- [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [A Language Agent for Autonomous Driving](https://mlsanity.com/paper/2311.10813/) (2023)
- [Language Prompt for Autonomous Driving](https://mlsanity.com/paper/2309.04379/) (2023)
- [Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2309.10228/) (2023)
- [Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2310.08034/) (2023)
- [Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)](https://mlsanity.com/paper/2505.16394/) (2025)
- [Towards Corner Case Detection for Autonomous Driving](https://mlsanity.com/paper/1902.09184/) (2019)
- [Description of Corner Cases in Automated Driving: Goals and Challenges](https://mlsanity.com/paper/2109.09607/) (2021)
- [Empowering Autonomous Driving with Large Language Models: A Safety Perspective](https://mlsanity.com/paper/2312.00812/) (2023)
- [Divide-or-Conquer? Which Part Should You Distill Your LLM?](https://mlsanity.com/paper/2402.15000/) (2024)
- [Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework](https://mlsanity.com/paper/2409.12812/) (2024)
- [On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation](https://mlsanity.com/paper/2411.11913/) (2024)
- [When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning](https://mlsanity.com/paper/2304.12046/) (2023)
- [Don't Get Stuck: A Deadlock Recovery Approach](https://mlsanity.com/paper/2408.10167/) (2024)
- [Efficient Path Planning in Large Unknown Environments with Switchable System Models for Automated Vehicles](https://mlsanity.com/paper/2310.06974/) (2023)

## Built on by (most cited first, 3 of 3)

- [SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving](https://mlsanity.com/paper/2512.10719/) (2025)
- [Revealing Interpretable Failure Modes of VLMs](https://mlsanity.com/paper/2605.12674/) (2026)
- [OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMs](https://mlsanity.com/paper/2606.22617/) (2026)

## Abstract (arXiv)

Despite significant advancements in recent decades, autonomous vehicles (AVs) continue to face challenges in navigating certain traffic scenarios where human drivers excel. In such situations, AVs often become immobilized, disrupting overall traffic flow. Current recovery solutions, such as remote intervention (which is costly and inefficient) and manual takeover (which excludes non-drivers and limits AV accessibility), are inadequate. This paper introduces StuckSolver, a novel Large Language Model (LLM) driven recovery framework that enables AVs to resolve immobilization scenarios through self-reasoning and/or passenger-guided decision-making. StuckSolver is designed as a plug-in add-on module that operates on top of the AV's existing perception-planning-control stack, requiring no modification to its internal architecture. Instead, it interfaces with standard sensor data streams to detect immobilization states, interpret environmental context, and generate high-level recovery commands that can be executed by the AV's native planner. We evaluate StuckSolver on the Bench2Drive benchmark and in custom-designed uncertainty scenarios. Results show that StuckSolver achieves near-state-of-the-art performance through autonomous self-reasoning alone and exhibits further improvements when passenger guidance is incorporated.