Edge Computing and the Future of the Connected Car
When thinking about a self-driving car, many think about the functions that can be remotely controlled via a smart phone, tablet, computer or a smart watch. Having the ability to remotely lock your car, find it in a busy parking lot, track it for theft or receive maintenance reminders on various systems is already seen as a modern day advancement. The goal of a connected automated vehicle is to improve the experience of the user, as most internet connected devices. However, the advantage of a connected car is not just its ability to connect to a smart device, but all of the information it can receive from the world around it.
Most connected cars available right now leverage cellular networks for connectivity. Mass streaming data that is generated over cellular networks is both cost and bandwidth prohibitive. While vehicles already use a multitude of sensors (like systems that notify the user of an approaching object when reversing), connected cars will require a lot more. In order for connected cars to provide the value they are intended to, there needs to be a device that can process this data in real time. Edge computing is the practice of processing data from IoT (Internet of Things) devices where it is generated. With edge, the data being collected gets analysed right at the source. It is then up to the technology that was installed by the OEM to analyse and translate what has been captured, what it means and where it will be stored. In many cases, this means that edge can translate raw signal data into actionable insights, or otherwise turn analog data into digital information. Some of the benefits of edge computing in connected automated driving include:
- eliminating the need for a centralised data-processing warehouse since the connected cars are processing the data that is being generated directly in the vehicle while it is driving
- promotes real time analytics without the lag since the data is being generated at the source, enabling the vehicle to perform while simultaneously reducing internet bandwidth
In connected cars, the data received is captured, consolidated and then sent to a real-time analytics platform in order to enrich the data. This provides the foundation for developing service center applications, sales reports and dashboard engineering. Using the results from the analytics platform, data scientists can train AI models to push the models back to the edge, enhancing the cars performance. Understanding the journey of that data from the edge to the AI will open up the possibilities for a truly connected future. Click here to read more.