This presentation will explore the shortcomings of current On-Board Diagnostics (OBD) systems in meeting the evolving needs of autonomous vehicles. It will advocate for a proactive vehicle maintenance strategy as crucial for ensuring the safety and reliability of these complex systems. The existing OBD model primarily reports existing symptoms or faults, rather than predicting potential component failures. This highlights the importance of predictive maintenance, yet significant challenges remain in acquiring high-quality data through On-Board Monitoring (OBM). These challenges encompass developing standards for anomaly definition, creating effective algorithms, ensuring data privacy, and mitigating the risks of tampering and cybersecurity. Potential solutions to be discussed include advanced sensing technologies, Artificial Intelligence (AI), and Machine Learning (ML) for predictive analytics and attack detection. Furthermore, the presentation will emphasize the necessity of collaboration among vehicle manufacturers, suppliers, and regulators to develop and implement effective predictive maintenance strategies for sustainable solutions.