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Large Multimodal Models (LMMs) have demonstrated exceptional comprehension and interpretation capabilities in Autonomous Driving (AD) by incorporating large language models.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Textual explanations for self-driving vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John Canny, and Zeynep Akata · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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Towards complexity level classification of driving scenarios using environmental information
Yongkang Liu and John HL Hansen · 2019
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A dynamical systems perspective on driver behavior
Jessica Hafetz Mirman · 2019
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Objects365: A large-scale, high-quality dataset for object detection
Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun · 2019
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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Explainable object-induced action decision for autonomous vehicles
Yiran Xu, Xiaoyin Yang, Lihang Gong, Hsuan-Chu Lin, Tz-Ying Wu, Yunsheng Li, and Nuno Vasconcelos · 2020
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Pali: A jointly-scaled multilingual language-image model
Xi Chen, Xiao Wang, Soravit Changpinyo, AJ Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, et al · 2022
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Expansion and shrinkage of localization for weakly-supervised semantic segmentation
Jinlong Li, Zequn Jie, Xu Wang, Xiaolin Wei, and Lin Ma · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Qwen-vl: A frontier large vision-language model with versatile abilities
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou · 2023
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Behavexplor: Behavior diversity guided testing for autonomous driving systems
Mingfei Cheng, Yuan Zhou, and Xiaofei Xie · 2023
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A survey on safety-critical driving scenario generation—a methodological perspective
Wenhao Ding, Chejian Xu, Mansur Arief, Haohong Lin, Bo Li, and Ding Zhao · 2023
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Planning-oriented autonomous driving
Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, et al · 2023
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Fuller: Unified multi-modality multi-task 3d perception via multi-level gradient calibration
Zhijian Huang, Sihao Lin, Guiyu Liu, Mukun Luo, Chaoqiang Ye, Hang Xu, Xiaojun Chang, and Xiaodan Liang · 2023
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Vad: Vectorized scene representation for efficient autonomous driving
Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang · 2023
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Drama: Joint risk localization and captioning in driving
Srikanth Malla, Chiho Choi, Isht Dwivedi, Joon Hee Choi, and Jiachen Li · 2023
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Dilu: A knowledge-driven approach to autonomous driving with large language models
Licheng Wen, Daocheng Fu, Xin Li, Xinyu Cai, Tao Ma, Pinlong Cai, Min Dou, Botian Shi, Liang He, and Yu Qiao · 2023
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A survey of large language models for autonomous driving
Zhenjie Yang, Xiaosong Jia, Hongyang Li, and Junchi Yan · 2023
Introducing llama 3.1: Our most capable models to date
AI Meta · 2024
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Vlp: Vision language planning for autonomous driving
Chenbin Pan, Burhaneddin Yaman, Tommaso Nesti, Abhirup Mallik, Alessandro G Allievi, Senem Velipasalar, and Liu Ren · 2024
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Vlaad: Vision and language assistant for autonomous driving
SungYeon Park, MinJae Lee, JiHyuk Kang, Hahyeon Choi, Yoonah Park, Juhwan Cho, Adam Lee, and DongKyu Kim · 2024
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Nuscenes-qa: A multi-modal visual question answering benchmark for autonomous driving scenario
Tianwen Qian, Jingjing Chen, Linhai Zhuo, Yang Jiao, and Yu-Gang Jiang · 2024
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Lmdrive: Closed-loop end-to-end driving with large language models
Hao Shao, Yuxuan Hu, Letian Wang, Guanglu Song, Steven L Waslander, Yu Liu, and Hongsheng Li · 2024
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Drivelm: Driving with graph visual question answering
Chonghao Sima, Katrin Renz, Kashyap Chitta, Li Chen, Hanxue Zhang, Chengen Xie, Jens Beißwenger, Ping Luo, Andreas Geiger, and Hongyang Li · 2024
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Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 2023
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A method for evaluating the complexity of test scenarios for autonomous vehicles
Lu Zhang, KongJian Qin, BoYa Zhou, and HuaSen Wang · 2023
Cited alongside, same era.
Maplm: A real-world large-scale vision-language benchmark for map and traffic scene understanding
Xu Cao, Tong Zhou, Yunsheng Ma, Wenqian Ye, Can Cui, Kun Tang, Zhipeng Cao, Kaizhao Liang, Ziran Wang, James M Rehg, et al · 2024
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Drive as you speak: Enabling human-like interaction with large language models in autonomous vehicles
Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, and Ziran Wang · 2024
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Holistic autonomous driving understanding by bird’s-eye-view injected multi-modal large models
Xinpeng Ding, Jianhua Han, Hang Xu, Xiaodan Liang, Wei Zhang, and Xiaomeng Li · 2024
Cited alongside, same era.
Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al · 2024
Cited alongside, same era.
Emma: End-to-end multimodal model for autonomous driving
Jyh-Jing Hwang, Runsheng Xu, Hubert Lin, Wei-Chih Hung, Jingwei Ji, Kristy Choi, Di Huang, Tong He, Paul Covington, Benjamin Sapp, et al · 2024
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Drivevlm: The convergence of autonomous driving and large vision-language models
Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Yang Wang, Zhiyong Zhao, Kun Zhan, Peng Jia, Xianpeng Lang, and Hang Zhao · 2024
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Drivegpt4: Interpretable end-to-end autonomous driving via large language model
Zhenhua Xu, Yujia Zhang, Enze Xie, Zhen Zhao, Yong Guo, Kwan-Yee K Wong, Zhenguo Li, and Hengshuang Zhao · 2024
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Robomm: All-in-one multimodal large model for robotic manipulation
Feng Yan, Fanfan Liu, Liming Zheng, Yufeng Zhong, Yiyang Huang, Zechao Guan, Chengjian Feng, and Lin Ma · 2024
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Embodied understanding of driving scenarios
Yunsong Zhou, Linyan Huang, Qingwen Bu, Jia Zeng, Tianyu Li, Hang Qiu, Hongzi Zhu, Minyi Guo, Yu Qiao, and Hongyang Li · 2024
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Making large language models better planners with reasoning-decision alignment
Zhijian Huang, Tao Tang, Shaoxiang Chen, Sihao Lin, Zequn Jie, Lin Ma, Guangrun Wang, and Xiaodan Liang · 2025
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Cross-modal and uncertainty-aware agglomeration for open-vocabulary 3d scene understanding
Jinlong Li, Cristiano Saltori, Fabio Poiesi, and Nicu Sebe · 2025
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Reason2drive: Towards interpretable and chain-based reasoning for autonomous driving
Ming Nie, Renyuan Peng, Chunwei Wang, Xinyue Cai, Jianhua Han, Hang Xu, and Li Zhang · 2025
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Shaoyuan Xie, Lingdong Kong, Yuhao Dong, Chonghao Sima, Wenwei Zhang, Qi Alfred Chen, Ziwei Liu, and Liang Pan · 2025
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Distime: Distribution-based time representation for video large language models
Yingsen Zeng, Zepeng Huang, Yujie Zhong, Chengjian Feng, Jie Hu, Lin Ma, and Yang Liu · 2025
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P3nav: A unified framework for embodied navigation integrating perception, planning, and prediction
Yufeng Zhong, Chengjian Feng, Feng Yan, Fanfan Liu, Liming Zheng, and Lin Ma · 2025
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