Sora for Intelligent Vehicles: A Step From Constraint-Based Simulation to Artificiofactual Experiments Through Dynamic Visualization

被引：1
作者：
Wang, Xumeng [1 ]
Xue, Xiao [2 ]
Yan, Ran [3 ]
Wang, Xingxia [4 ,5 ]
Di, Yining [6 ]
Chen, Wei [7 ]
Wang, Fei-Yue [4 ,5 ]
机构：
[1] Nankai Univ, Coll Cyber Sci, Tianjin 300381, Peoples R China
[2] Tianjin Univ, Coll Intelligence & Comp, Sch Comp Software, Tianjin 300072, Peoples R China
[3] Nanyang Technol Univ, Sch Civil & Environm Engn, Singapore 639798, Singapore
[4] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
[5] Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
[6] Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Hong Kong, Peoples R China
[7] Zhejiang Univ, State Key Lab CAD&CG, Zhejiang 310058, Peoples R China
来源：
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES | 2024年 / 9卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Intelligent vehicles; artificiofactual experiments; Sora; dynamic visualization; ARTIFICIAL-INTELLIGENCE; TRANSPORTATION SYSTEMS; TRENDS;
D O I：
10.1109/TIV.2024.3384550
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Scenario simulation plays an integral role in the development, application, and management of intelligent vehicles. However, planning agents and customizing scenarios for complex systems are laborious, making it challenging to implement high-performance simulations. The striking progress made by Sora, a large-scale text-to-video model, suggests a research opportunity for high-performance simulation through dynamic visualizations. This paper reports the prospective effects of Sora on the scenario simulation of intelligent vehicles. Specifically, we review the achievements of Sora, picture the perspectives of artificiofactual experiments on intelligent vehicles based on the performance of Sora-type techniques, and discuss how far are we now.
引用
收藏
页码：4249 / 4253
页数：5
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