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ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction

文献类型：期刊论文

作者Li, Xuan1; Liu, Enlu2; Shen, Tianyu3; Huang, Jun4; Wang, Fei-Yue5
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期2024-03-01
卷号9期号:3页码:4422-4431
关键词Parallel driving scenarios engineering foundation model vehicle operating system generative pre-trained transformer trajectory prediction
ISSN号2379-8858
DOI10.1109/TIV.2024.3363232
通讯作者Shen, Tianyu(tianyu.shen@buct.edu.cn) ; Wang, Fei-Yue(feiyue@ieee.org)
英文摘要The latest developments in parallel driving foreshadow the possibility of delivering intelligence across organizations using foundation models. As is well-known, there are limitations in scenario acquisition in the field of intelligent vehicles (IV), such as efficiency, diversity, and complexity, which hinder in-depth research of vehicle intelligence. To address this issue, this manuscript draws inspiration from scenarios engineering, parallel driving and introduces a pioneering framework for scenario generation, leveraging the ChatGPT, denoted as SeGPT. Within this framework, we define a trajectory scenario and design prompts engineering to generate complex and challenging scenarios. Furthermore, SeGPT, in combination with "Three Modes", foundation models, vehicle operating system, and other advanced infrastructure, holds the potential to achieve higher levels of autonomous driving. Experimental outcomes substantiate SeGPT's adeptness in producing a spectrum of varied scenarios, underscoring its potential to augment the development of trajectory prediction algorithms. These findings mark significant progress in the domain of scenario generation, also pointing towards new directions in the research of vehicle intelligence and scenarios engineering.
WOS关键词INTELLIGENT ; VISION ; SYSTEM
资助项目National Natural Science Foundation of China
WOS研究方向Computer Science ; Engineering ; Transportation
语种英语
WOS记录号WOS:001214544700027
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/58379]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Shen, Tianyu; Wang, Fei-Yue
作者单位1.Peng Cheng Lab, Shenzhen 518000, Peoples R China
2.Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha 410001, Peoples R China
3.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
4.Macau Univ Sci & Technol, Macau 999078, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Xuan,Liu, Enlu,Shen, Tianyu,et al. ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2024,9(3):4422-4431.
APA Li, Xuan,Liu, Enlu,Shen, Tianyu,Huang, Jun,&Wang, Fei-Yue.(2024).ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,9(3),4422-4431.
MLA Li, Xuan,et al."ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 9.3(2024):4422-4431.

入库方式： OAI收割

来源：自动化研究所

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