The future automotive industry demands massive data collection, enhanced real-time processing such as sub-second latency AI inference, high availability, and large-scale data analytics for training machine learning and AI models at petabyte to exabyte scales. Traditional centralized clouds face challenges with data transfer loads, latency, reliability, and environmental impact, making them inadequate for these needs.

To address this, the Automotive Edge Computing Consortium (AECC) promotes widespread adoption of distributed edge computing architectures. By decentralizing data processing across geographically dispersed edge infrastructures, latency is minimized, reliability is improved through redundancy, and energy efficiency is increased by leveraging local renewable energy. However, significant gaps remain in building a fully integrated edge ecosystem supporting diverse automotive use cases.

This Industry Blueprint provides comprehensive guidance for adopting edge computing in the automotive sector. It clarifies the design principles and solutions for a holistic, end-to-end distributed architecture that integrates networking, computing and data management. Key use cases include intelligent driving, teleoperated driving, high-definition mapping, voice-interactive AI agents, digital twins, green mobility, and data-driven development platforms.

Download AECC’s Industry Blueprint now for more on key challenges in this space, a look at the limitations of current technology, and an exploration of the need for innovation.