Co-Pilot AI: Intelligent Vehicle Assistant with Predictive Diagnostics and Voice-Activated Controls

This paper presents the design and implementation of an intelligent vehicle dashboard that integrates real-time On-Board Diagnostics (OBD-II) data with a voice-driven assistant for enhanced driver interaction. The system employs a Raspberry Pi 5 platform connected to the vehicle’s OBD-II interface via an ELM327 adapter, enabling continuous monitoring of key performance metrics. To ensure reliable data storage, a dual- logging strategy is implemented, utilizing both CSV files and a local SQLite database with a comprehensive schema design. A modular Python Flask backend facilitates seamless data retrieval for the voice assistant, leveraging OpenAI’s 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 system’s 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 system’s 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.