Despite the strong interests in creating data economy, automotive industries are creating data silos with each stakeholder maintaining its own data cloud. Federated learning (FL), designed for privacy-preserving collaborative Machine Learning (ML), offers a promising method that allows multiple stakeholders to share information through ML models without the exposure of raw data, thus natively protecting privacy. Motivated by the strong need for automotive collaboration and the advancement of FL, this paper investigates how FL could enable privacy-preserving information sharing for automotive industries. We first introduce the statuses and challenges for automotive data sharing, followed by a brief introduction to FL. We then present a comprehensive discussion on potential applications of federated learning to enable an automotive collaborative ecosystem. To illustrate the benefits, we apply FL for driver action classification and demonstrate the potential for collaborative machine learning without data sharing.