IDAS: Intelligent Driving Assistance System Using RAG
In the fast-growing automotive technology sector, it has become increasingly clear that there is a need for cars with smarter and more interactive systems. This article presents the Intelligent Driving Assistance System (IDAS), an artificial intelligence system that enables the driver to use voice c...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10643289/ |
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| author | Hernandez-Salinas Bernardo Juan Terven E. A. Chavez-Urbiola Diana-Margarita Cordova-Esparza Julio-Alejandro Romero-Gonzalez Amadeo Arguelles Ilse Cervantes |
| author_facet | Hernandez-Salinas Bernardo Juan Terven E. A. Chavez-Urbiola Diana-Margarita Cordova-Esparza Julio-Alejandro Romero-Gonzalez Amadeo Arguelles Ilse Cervantes |
| author_sort | Hernandez-Salinas Bernardo |
| collection | DOAJ |
| description | In the fast-growing automotive technology sector, it has become increasingly clear that there is a need for cars with smarter and more interactive systems. This article presents the Intelligent Driving Assistance System (IDAS), an artificial intelligence system that enables the driver to use voice commands to access various features of a car. The primary component of IDAS is a Large Language Model (LLM), which, through retrieval augmented generation (RAG), can efficiently read and understand the car manual for immediate context-based aid. In addition, this system incorporates speech recognition and speech synthesis capabilities, it can understand commands given in multiple languages, improving user experiences among diverse driver communities. Our results show a minimum response time of one second for the pipeline using GPT-4o-mini and Mistral Nemo. |
| format | Article |
| id | doaj-art-5f1132325fe240da8c92948912e83938 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-5f1132325fe240da8c92948912e839382025-01-30T00:04:09ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-0151139116510.1109/OJVT.2024.344744910643289IDAS: Intelligent Driving Assistance System Using RAGHernandez-Salinas Bernardo0Juan Terven1https://orcid.org/0000-0001-6662-0390E. A. Chavez-Urbiola2https://orcid.org/0000-0002-1938-8610Diana-Margarita Cordova-Esparza3https://orcid.org/0000-0002-5657-7752Julio-Alejandro Romero-Gonzalez4https://orcid.org/0000-0001-7257-7595Amadeo Arguelles5Ilse Cervantes6https://orcid.org/0000-0003-3478-9241Instituto Politecnico Nacional, CICATA, Querétaro, MexicoInstituto Politecnico Nacional, CICATA, Querétaro, MexicoInstituto Politecnico Nacional, CICATA, Querétaro, MexicoFacultad de Informatica, Universidad Autónoma de Queretaro, Queretaro, MexicoFacultad de Informatica, Universidad Autónoma de Queretaro, Queretaro, MexicoInstituto Politecnico Nacional, Centro de Investigacion en Computacion, Mexico City, MexicoInstituto Politecnico Nacional, CICATA, Querétaro, MexicoIn the fast-growing automotive technology sector, it has become increasingly clear that there is a need for cars with smarter and more interactive systems. This article presents the Intelligent Driving Assistance System (IDAS), an artificial intelligence system that enables the driver to use voice commands to access various features of a car. The primary component of IDAS is a Large Language Model (LLM), which, through retrieval augmented generation (RAG), can efficiently read and understand the car manual for immediate context-based aid. In addition, this system incorporates speech recognition and speech synthesis capabilities, it can understand commands given in multiple languages, improving user experiences among diverse driver communities. Our results show a minimum response time of one second for the pipeline using GPT-4o-mini and Mistral Nemo.https://ieeexplore.ieee.org/document/10643289/Artificial intelligencehuman-computer interactionintelligent agentslarge language modelsretrieval augmented generation (RAG) |
| spellingShingle | Hernandez-Salinas Bernardo Juan Terven E. A. Chavez-Urbiola Diana-Margarita Cordova-Esparza Julio-Alejandro Romero-Gonzalez Amadeo Arguelles Ilse Cervantes IDAS: Intelligent Driving Assistance System Using RAG IEEE Open Journal of Vehicular Technology Artificial intelligence human-computer interaction intelligent agents large language models retrieval augmented generation (RAG) |
| title | IDAS: Intelligent Driving Assistance System Using RAG |
| title_full | IDAS: Intelligent Driving Assistance System Using RAG |
| title_fullStr | IDAS: Intelligent Driving Assistance System Using RAG |
| title_full_unstemmed | IDAS: Intelligent Driving Assistance System Using RAG |
| title_short | IDAS: Intelligent Driving Assistance System Using RAG |
| title_sort | idas intelligent driving assistance system using rag |
| topic | Artificial intelligence human-computer interaction intelligent agents large language models retrieval augmented generation (RAG) |
| url | https://ieeexplore.ieee.org/document/10643289/ |
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