VistaRAG: Toward Safe and Trustworthy Autonomous Driving Through Retrieval-Augmented Generation
文献类型：期刊论文
| 作者 | Dai, Xingyuan2,3,4 ; Guo, Chao3; Tang, Yun5; Li, Haichuan1; Wang, Yutong3; Huang, Jun6; Tian, Yonglin3; Xia, Xin7; Lv, Yisheng3; Wang, Fei-Yue8
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
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| 出版日期 | 2024-04-01 |
| 卷号 | 9期号:4页码:4579-4582 |
| 关键词 | Databases Autonomous vehicles Decision making Safety Data models Real-time systems Reliability Intelligent vehicles foundation models retrieval- augmented generation (RAG) |
| ISSN号 | 2379-8858 |
| DOI | 10.1109/TIV.2024.3396450 |
| 通讯作者 | Dai, Xingyuan(xingyuan.dai@ia.ac.cn) |
| 英文摘要 | Autonomous driving based on foundation models has recently garnered widespread attention. However, the risk of hallucinations inherent in foundation models could compromise the safety and reliability of autonomous driving systems. This letter, as part of a series of reports from the Distributed/Decentralized Hybrid Workshop on Foundation/Infrastructure Intelligence (DHW-FII), aims to tackle these issues. We introduce VistaRAG, which integrates retrieval-augmented generation (RAG) technologies into autonomous driving systems based on foundation models, to address the inherent reliability challenges in decision-making. VistaRAG employs a dynamic retrieval mechanism to access highly relevant driving experience, real-time road network status, and other contextual information from external databases. This aids foundation models in informed reasoning and decision-making, thereby enhancing the safety and trustworthiness of foundation-model-based autonomous driving systems under complex traffic scenarios. |
| 资助项目 | National Key R&D Program of China |
| WOS研究方向 | Computer Science ; Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001250038700013 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key R&D Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59121]  |
| 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 | Dai, Xingyuan |
| 作者单位 | 1.Univ Turku, Dept Comp, Turku 20014, Finland 2.Changan Univ, Engn Res Ctr Highway Infrastruct Digitalizat, Xian 710064, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 4.Shanghai Artificial Intelligence Lab, Shanghai 20030, Peoples R China 5.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore 6.Macau Univ Sci & Technol, Macau 999078, Peoples R China 7.UCLA, UCLA Mobil Lab, Dept Civil & Environm Engn, Los Angeles, CA 90095 USA 8.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 | Dai, Xingyuan,Guo, Chao,Tang, Yun,et al. VistaRAG: Toward Safe and Trustworthy Autonomous Driving Through Retrieval-Augmented Generation[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2024,9(4):4579-4582. |
| APA | Dai, Xingyuan.,Guo, Chao.,Tang, Yun.,Li, Haichuan.,Wang, Yutong.,...&Wang, Fei-Yue.(2024).VistaRAG: Toward Safe and Trustworthy Autonomous Driving Through Retrieval-Augmented Generation.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,9(4),4579-4582. |
| MLA | Dai, Xingyuan,et al."VistaRAG: Toward Safe and Trustworthy Autonomous Driving Through Retrieval-Augmented Generation".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 9.4(2024):4579-4582. |
入库方式： OAI收割
来源：自动化研究所
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