A Survey on Multimodal Large Language Models for Autonomous Driving-Reference-Cited by-同舟云学术

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A Survey on Multimodal Large Language Models for Autonomous Driving

Published:2024-01-01 Issue: Volume: Page:
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Container-title:2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
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Author:

Cui Can¹,Ma Yunsheng¹,Cao Xu²,Ye Wenqian³,Zhou Yang⁴,Liang Kaizhao⁵,Chen Jintai²,Lu Juanwu¹,Yang Zichong¹,Liao Kuei-Da⁶,Gao Tianren⁵,Li Erlong⁷,Tang Kun⁷,Cao Zhipeng⁷,Zhou Tong⁷,Liu Ao⁷,Yan Xinrui⁷,Mei Shuqi⁷,Cao Jianguo⁸,Wang Ziran¹,Zheng Chao⁷

Affiliation:

1. Purdue University,West Lafayette,IN,USA,47907

2. University of Illinois Urbana-Champaign,Champaign,IL,USA,61801

3. University of Virginia,Charlottesville,VA,USA,22903

4. New York University,New York,NY,USA,11201

5. SambaNova Systems, Inc,Palo Alto,CA,USA,94303

6. Objective, Inc,San Francisco,CA,USA,94110

7. Tencent T Lab,Beijing,China

8. PediaMed AI,Shenzhen,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10495586/10495587/10495592.pdf?arnumber=10495592

Reference211 articles.

1. Generating Adversarial Driving Scenarios in High-Fidelity Simulators

2. Flamingo: a Visual Language Model for Few-Shot Learning;Alayrac;NeurIPS,2022

3. Self-supervised multimodal versatile networks;Alayrac;Advances in Neural Information Processing Systems,2020

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1. Large models for intelligent transportation systems and autonomous vehicles: A survey;Advanced Engineering Informatics;2024-10

2. When large language model meets optimization;Swarm and Evolutionary Computation;2024-10

3. Hallucination Reduction and Optimization for Large Language Model-Based Autonomous Driving;Symmetry;2024-09-11

4. Using Multimodal Large Language Models (MLLMs) for Automated Detection of Traffic Safety-Critical Events;Vehicles;2024-09-02

5. FFA: Foreground Feature Approximation Digitally against Remote Sensing Object Detection;Remote Sensing;2024-08-29

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