Abstract: In this paper, we establish one key area where predictive analytics can bring value to the consumers of connected car platforms using state-of-the-art machine learning (ML) techniques such as long short-term memory (LSTM) networks. In addition to providing an idea of the kinks and challenges in the application and deployment of AI-driven predictive algorithms, we also describe some best practices that are essential to ensure that the AI-driven insights manifest themselves without compromising much on their accuracy and reliability. Though applied to the predictive insights associated with vehicle maintenance, the tools and practices described in this paper are generic. They can be used in similar contexts for predictive insights associated with other areas of connected car platforms. Connected car solutions have become one of the essential parts of the Internet of Things (IoT) and will continue to be a driving force behind innovation in the automotive industry. With the growth of Advanced Driver Assistance Systems (ADAS), in-car infotainment systems, and the continued evolution of automotive technologies targeting connected and automated driving, the industry is witnessing another wave of innovation in connected car platforms. Predictive insights can provide tangible value and benefits to the consumers of the connected car platforms. AI-driven predictive analytics bring great potential in harnessing connected car data to generate these valuable insights.

Keywords: Connected Car Platforms, Predictive Analysis, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM)

| DOI: 10.17148/IARJSET.2020.71216