Paper Contents
Abstract
This paper presents the design and implementation of an intelligent vehicle dashboard that integrates real-time OnBoard Diagnostics (OBD-II) data with a voice-driven assistant for enhanced driver interaction. The system employs a Raspberry Pi 5 platform connected to the vehicles OBD-II interface via an ELM327 adapter, enabling continuous monitoring of key performance metrics. To ensure reliable data storage, a duallogging strategy is implemented, utilizing both CSV files and alocal SQLite database with a comprehensive schema design. A modular Python Flask backend facilitates seamless data retrieval for the voice assistant, leveraging OpenAIs Whisper model for speech-to-text (STT) conversion and Claude 3.7 Sonnet for natural language processing (NLP). Real-world testing across five different vehicle models demonstrates the systems ability to provide context-aware responses, including real-time performance metrics and predictive maintenance alerts. With a total response time averaging 8.16 seconds and balanced resource utilization, this research offers a practical architecture with potential applications in predictive maintenance and personalized vehicle experiences. The systems innovative integration of hardware sensors, cloud APIs, and edge computing creates a platform that significantly enhances the driving experience while addressing key challenges in modern automotive interfaces.
Copyright
Copyright © 2025 Mr R Mohankumar. This is an open access article distributed under the Creative Commons Attribution License.
