LLM在自动驾驶中的Survey-优快云博客

作者 | erkang 编辑 | 自动驾驶之心

原文链接：https://zhuanlan.zhihu.com/p/707464289

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做了粗略的LLM在自动驾驶中的survey

汇总了相关的paper list
方便快速了解信息，摘取了abstract，图-问题描述，图-方法overview
TODO：归纳分类 & 极简总结

整体感觉，目前的LLM for Autonomous Driving虽然很火，在三维世界理解下的reasoning还是大有可为，尤其是为端到端服务。

Sec-1: Paper list

[1] LMDrive: Closed-loop End-to-End Driving with Large language Models
[2] Driving Everywhere with Large Language Model Policy Adaptation
[3] ADAPT: Action-aware Driving Caption Transformer
[4] Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs
[5] DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experience
[6] Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Model
[7] DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model
[8] ADriver-I: A General World Model for Autonomous Driving
[9] DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving
[10] Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving
[11] Embodied Understanding of Driving Scenarios
[12] Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models
[13] OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driving with 3D Perception, Reasoning and Planning
[14] Dolphins: Multimodal Language Model for Driving
[15] HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving
[16] Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving
[17] RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model
[18 ]AD-H: Autonomous Driving with Hierarchical Agents
[19] Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
[20] LingoQA: Video Question Answering for Autonomous Driving
[21] 3D Dense Captioning beyond Nouns: A Middleware for Autonomous Driving
[22] Probing Multimodal LLMs as World Models for Driving
[23] LeGo-Drive: Language-enhanced Goal-oriented Closed-Loop End-to-End Autonomous Driving
[24] LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding｜
[25] DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving

Sec-2: Paper汇总

包含Title, Venue, Year, Institute, Abstract, Fig-Problem-define, and Fig-Method-Overview

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