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A Survey on Multimodal Large Language Models for Autonomous Driving

We add new references from CVPR 2024 in our repo, some references are from 自动驾驶之心.

:boom: News: MAPLM (Tencent, UIUC) and LaMPilot (Purdue University) from our team are accepted by CVPR 2024.

News: LLVM-AD Workshop is successfully organized at WACV 2024.

WACV 2024 Proceedings | Arxiv | Workshop | Report by 机器之心

Summary of the 1st WACV Workshop on Large Language and Vision Models for Autonomous Driving (LLVM-AD)

Abstract

With the emergence of Large Language Models (LLMs) and Vision Foundation Models (VFMs), multimodal AI systems benefiting from large models have the potential to equally perceive the real world, make decisions, and control tools as humans. In recent months, LLMs have shown widespread attention in autonomous driving and map systems. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors to apply in LLM driving systems. In this repo, we present a systematic investigation in this field. We first introduce the background of Multimodal Large Language Models (MLLMs), the multimodal models development using LLMs, and the history of autonomous driving. Then, we overview existing MLLM tools for driving, transportation, and map systems together with existing datasets and benchmarks. Moreover, we summarized the works in The 1st WACV Workshop on Large Language and Vision Models for Autonomous Driving (LLVM-AD), which is the first workshop of its kind regarding LLMs in autonomous driving. To further promote the development of this field, we also discuss several important problems regarding using MLLMs in autonomous driving systems that need to be solved by both academia and industry.

Awesome Papers

MLLM for Perception & Planning & Control for Autonomous Driving

Please ping us if you find any interesting new papers in this area. We will update them into the Table. And all of them will be included in the next version of the survey paper.

Datasets

The table is inspired by Comparison and stats in DriveLM

Other Survey Papers

Papers Accepted by WACV 2024 LLVM-AD

A Survey on Multimodal Large Language Models for Autonomous Driving

Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles

Drive Like a Human: Rethinking Autonomous Driving with Large Language Models

A Game of Bundle Adjustment - Learning Efficient Convergence Accepted as a tech report for their ICCV 2023 Paper

VLAAD: Vision and Language Assistant for Autonomous Driving

A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with Dynamic Obstacle Trajectory Prediction and Its Application with LLMs

Human-Centric Autonomous Systems With LLMs for User Command Reasoning

NuScenes-MQA: Integrated Evaluation of Captions and QA for Autonomous Driving Datasets using Markup Annotations

Latency Driven Spatially Sparse Optimization for Multi-Branch CNNs for Semantic Segmentation

LIP-Loc: LiDAR Image Pretraining for Cross-Modal Localization

Future Directions Section

Social Behavior for Autonomous Driving (UIUC, Purdue University)

Personalized Autonomous Driving (Purdue University, UIUC)

Hardware Support for LLMs in Autonomous Driving (SambaNova Systems)

LLMs for HD Maps (Tencent)

Code as Action for Autonomous Driving (Purdue University, UIUC)

Citation

If the survey and our workshop inspire you, please cite our work:

@inproceedings{cui2024survey,
  title={A survey on multimodal large language models for autonomous driving},
  author={Cui, Can and Ma, Yunsheng and Cao, Xu and Ye, Wenqian and Zhou, Yang and Liang, Kaizhao and Chen, Jintai and Lu, Juanwu and Yang, Zichong and Liao, Kuei-Da and others},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={958--979},
  year={2024}
}