Towards knowledge-driven autonomous driving

[PDF] arxiv.org

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 …
被引用次数：51 相关文章 所有 3 个版本
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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 …
被引用次数：24 相关文章 所有 2 个版本
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If llm is the wizard, then code is the wand: A survey on how code empowers large language models to serve as intelligent agents

K Yang, J Liu, J Wu, C Yang, YR Fung, S Li… - arXiv preprint arXiv …, 2024 - arxiv.org
The prominent large language models (LLMs) of today differ from past language models not
only in size, but also in the fact that they are trained on a combination of natural language …
被引用次数：19 相关文章 所有 2 个版本

VistaRAG: Toward Safe and Trustworthy Autonomous Driving Through Retrieval-Augmented Generation

X Dai, C Guo, Y Tang, H Li, Y Wang… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Autonomous driving based on foundation models has recently garnered widespread
attention. However, the risk of hallucinations inherent in foundation models could …
被引用次数：3 相关文章
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Resimad: Zero-shot 3d domain transfer for autonomous driving with source reconstruction and target simulation

B Zhang, X Cai, J Yuan, D Yang, J Guo, R Xia… - arXiv preprint arXiv …, 2023 - arxiv.org
Domain shifts such as sensor type changes and geographical situation variations are
prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on …
被引用次数：4 相关文章 所有 3 个版本
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Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

M Xu, D Niyato, J Kang, Z Xiong, A Jamalipour… - arXiv preprint arXiv …, 2024 - arxiv.org
Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of
intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets …
被引用次数：1 相关文章 所有 2 个版本
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Learn To be Efficient: Build Structured Sparsity in Large Language Models

H Zheng, X Bai, B Chen, F Lai, A Prakash - arXiv preprint arXiv …, 2024 - arxiv.org
Large Language Models (LLMs) have achieved remarkable success with their billion-level
parameters, yet they incur high inference overheads. The emergence of activation sparsity in …
被引用次数：3 相关文章 所有 2 个版本
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Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving

J Mei, Y Ma, X Yang, L Wen, X Cai, X Li, D Fu… - arXiv preprint arXiv …, 2024 - arxiv.org
Autonomous driving has advanced significantly due to sensors, machine learning, and
artificial intelligence improvements. However, prevailing methods struggle with intricate …
相关文章 所有 2 个版本
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LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving

D Fu, W Lei, L Wen, P Cai, S Mao, M Dou, B Shi… - arXiv preprint arXiv …, 2024 - arxiv.org
The emergence of Multimodal Large Language Models ((M) LLMs) has ushered in new
avenues in artificial intelligence, particularly for autonomous driving by offering enhanced …
被引用次数：4 相关文章 所有 3 个版本
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Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives

S Luo, W Chen, W Tian, R Liu, L Hou, X Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
Foundation models have indeed made a profound impact on various fields, emerging as
pivotal components that significantly shape the capabilities of intelligent systems. In the …
被引用次数：1 相关文章 所有 2 个版本