New consortium looks at the future of automotive edge computing

The development of self-driving cars and automotive edge computing has been a hot topic in recent years. It has spurred a global effort to push the limits of technology, with organizations and companies working together to create the best solutions. The Automotive Edge Computing Consortium (AECC) is an example of such a collaborative effort that is making strides in the field. In this post, we will explore the concept of automotive edge computing and the role of AECC in its development.

Automotive Edge Computing

It is no longer news that cars are no longer just about transportation. They have become mobile data centers due to their increasing connectivity, which has opened up a whole new world of possibilities. Automotive edge computing aims to leverage this connectivity intelligently to provide better driver experiences and more efficient operations. In simpler terms, it involves processing data that is generated within the car locally, rather than sending all of it to the cloud, which can be expensive and time-consuming.

The concept of edge computing is not new, but it has become increasingly important in recent years, especially in the context of the Internet of Things (IoT). With an increasing number of connected devices generating vast amounts of data, it has become necessary to process some of this data locally, at the edge of the network, to reduce latency and control costs. The same goes for the automotive industry. As vehicles become more connected, they generate vast amounts of data that can be used for various purposes, such as optimizing traffic flow, enabling predictive maintenance, and enhancing driver safety.

The Role of AECC

The automotive industry has recognized the potential of automotive edge computing, and several organizations and standards bodies have emerged to drive its development. One of such organizations is the Automotive Edge Computing Consortium (AECC).

AECC was formed in 2018, as a global initiative to develop an ecosystem for connected cars. The consortium aims to create an open, scalable, and secure platform for the automotive industry to deploy connected car services. It brings together some of the biggest names in the automotive industry, such as Toyota, Nissan, and Honda, along with leading technology companies like Intel and Huawei.

The primary goal of AECC is to define a standardized interface between edge computing nodes and cloud computing infrastructure. This interface will enable seamless communication and data exchange between connected cars and cloud services, while still providing low latency, high bandwidth, and secure data transfer.

Abstract

Automotive edge computing is a concept that has gained widespread attention in recent years. It involves processing data generated by cars locally, at the edge of the network, to provide better driver experiences and more efficient operations. The Automotive Edge Computing Consortium (AECC) is a global initiative that aims to develop an ecosystem for connected cars. It brings together leading automotive industry players and technology companies to create an open, scalable, and secure platform for the automotive industry to deploy connected car services.

Introduction

The automotive industry has undergone tremendous changes in recent years. New technologies such as self-driving cars, electric powertrains, and connected car services have emerged, transforming the way we interact with cars. These technologies have also created new challenges, such as managing vast amounts of data generated by connected cars and ensuring the security and privacy of this data. Automotive edge computing is a concept that seeks to address these challenges by processing data locally, at the edge of the network, rather than sending it all to the cloud. This post aims to explore the concept of automotive edge computing and the role of the Automotive Edge Computing Consortium (AECC) in its development.

Content

The concept of edge computing is not new. It has been around for some time, but it has gained considerable attention in recent years, mainly due to the proliferation of the Internet of Things (IoT). Edge computing involves processing data at the network edge, closer to the source of the data, rather than sending it all to a central data center or cloud. This approach offers several benefits, such as reducing latency, reducing bandwidth consumption, and improving security and privacy.

In the context of the automotive industry, edge computing offers significant opportunities for improving driver experiences, enhancing safety and security, and optimizing operations. Connected cars generate vast amounts of data, such as location data, sensor data, and telematics data, which can be used for various purposes. However, sending all this data to the cloud for processing can be expensive, both in terms of cost and time. Edge computing provides a solution by processing some of this data locally, within the car or at the edge of the network.

The potential of automotive edge computing has not gone unnoticed in the industry. Several organizations and standards bodies have emerged to drive its development, with AECC being a prominent example. AECC brings together leading automotive industry players such as Toyota, Nissan, and Honda, along with leading technology companies like Intel and Huawei, to create an open, scalable, and secure platform for the automotive industry to deploy connected car services.

The primary goal of AECC is to define a standardized interface between edge computing nodes and cloud computing infrastructure. This interface will enable seamless communication and data exchange between connected cars and cloud services while still providing low latency, high bandwidth, and secure data transfer. To achieve this goal, AECC has defined several workstreams that focus on various aspects of the ecosystem, such as security, interoperability, and reliability.

One of the critical areas of focus for AECC is security. Connected cars are highly desirable targets for hackers and cybercriminals, given the valuable data they generate. This data can be used for various malicious purposes, such as stealing identities, causing accidents, or disrupting traffic. Therefore, AECC is working to define standardized security protocols and best practices that can be implemented across the ecosystem to ensure the security and privacy of connected car data.

Another critical area of focus for AECC is interoperability. The automotive industry is highly fragmented, with different players using different technologies and protocols. This fragmentation can create barriers to entry for new players, hindering innovation and stalling progress. Therefore, AECC is developing a standardized interface that can enable seamless communication and data exchange between different connected car systems and cloud services, regardless of the underlying technology or protocol.

Reliability is also an essential aspect of the connected car ecosystem. Connected cars are expected to operate reliably and consistently, even in harsh environments and under adverse conditions. To ensure this, AECC is working to define standardized reliability standards and best practices that can be implemented across the ecosystem.

Conclusion

The Automotive Edge Computing Consortium (AECC) is a global initiative that aims to develop an ecosystem for connected cars. It brings together leading automotive industry players and technology companies to create an open, scalable, and secure platform for the automotive industry to deploy connected car services. Automotive edge computing offers significant opportunities for improving driver experiences, enhancing safety and security, and optimizing operations. However, realizing these opportunities requires addressing several challenges, such as security, interoperability, and reliability. AECC is working to define standardized protocols and best practices that can enable seamless communication and data exchange between connected cars and cloud services while ensuring the security, privacy, and reliability of connected car data.