Language prompt for autonomous driving

D Wu, W Han, T Wang, Y Liu, X Zhang… - arXiv preprint arXiv …, 2023 - arxiv.org
A new trend in the computer vision community is to capture objects of interest following
flexible human command represented by a natural language prompt. However, the progress …

Languagempc: Large language models as decision makers for autonomous driving

H Sha, Y Mu, Y Jiang, L Chen, C Xu, P Luo… - arXiv preprint arXiv …, 2023 - arxiv.org
Existing learning-based autonomous driving (AD) systems face challenges in
comprehending high-level information, generalizing to rare events, and providing …

A survey of large language models for autonomous driving

Z Yang, X Jia, H Li, J Yan - arXiv preprint arXiv:2311.01043, 2023 - arxiv.org
Autonomous driving technology, a catalyst for revolutionizing transportation and urban
mobility, has the tend to transition from rule-based systems to data-driven strategies …

Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving

W Wang, J Xie, CY Hu, H Zou, J Fan, W Tong… - arXiv preprint arXiv …, 2023 - arxiv.org
Large language models (LLMs) have opened up new possibilities for intelligent agents,
endowing them with human-like thinking and cognitive abilities. In this work, we delve into …

Lampilot: An open benchmark dataset for autonomous driving with language model programs

Y Ma, C Cui, X Cao, W Ye, P Liu, J Lu… - Proceedings of the …, 2024 - openaccess.thecvf.com
Autonomous driving (AD) has made significant strides in recent years. However existing
frameworks struggle to interpret and execute spontaneous user instructions such as" …

Dilu: A knowledge-driven approach to autonomous driving with large language models

L Wen, D Fu, X Li, X Cai, T Ma, P Cai, M Dou… - arXiv preprint arXiv …, 2023 - arxiv.org
Recent advancements in autonomous driving have relied on data-driven approaches, which
are widely adopted but face challenges including dataset bias, overfitting, and …

Drivegpt4: Interpretable end-to-end autonomous driving via large language model

Z Xu, Y Zhang, E Xie, Z Zhao, Y Guo, KKY Wong… - arXiv preprint arXiv …, 2023 - arxiv.org
In the past decade, autonomous driving has experienced rapid development in both
academia and industry. However, its limited interpretability remains a significant unsolved …

Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving

V Dewangan, T Choudhary, S Chandhok… - arXiv preprint arXiv …, 2023 - arxiv.org
Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps
in autonomous driving contexts. While existing perception systems for autonomous driving …

Lmdrive: Closed-loop end-to-end driving with large language models

H Shao, Y Hu, L Wang, G Song… - Proceedings of the …, 2024 - openaccess.thecvf.com
Despite significant recent progress in the field of autonomous driving modern methods still
struggle and can incur serious accidents when encountering long-tail unforeseen events …

Receive, reason, and react: Drive as you say, with large language models in autonomous vehicles

C Cui, Y Ma, X Cao, W Ye… - IEEE Intelligent …, 2024 - ieeexplore.ieee.org
The fusion of human-centric design and artificial intelligence capabilities has opened up
new possibilities for next-generation autonomous vehicles that go beyond traditional …