In this thesis, a context-aware, personalized virtual assistant for use in automobiles is presented. With the increasing use of technology in automobiles, there is a growing need for safer and more practical ways for drivers to access information and perform tasks while driving. Voice-based interfaces, such as natural language processing, provide a solution to this problem as they do not require visual or manual input. In this thesis, a fine-tuned model of GPT-3 is used to understand user intentions and identify the user’s needs. The voice assistant is trained to understand the
environment and the actions it can perform. The use of triggers such as drowsiness detection is also implemented to make the virtual assistant proactive in ensuring the user’s safety. User testing and evaluation was conducted to demonstrate the effectiveness of the context-aware, personalized virtual assistant in improving the driving experience and promoting safe driving practices.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:87788 |
| Date | 30 October 2023 |
| Creators | Rastogi, Utkarsh |
| Contributors | Hardt, Wolfram, Laribi, Zied, Heller, Ariane, Technische Universität Chemnitz |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0018 seconds