NXP Semiconductors has integrated edge- and cloud-based compute services from AWS into its new S32G vehicle network processor, for in-car communications gateways, to help enterprises, notably car makers and insurance companies, to process, share, and commercialise vehicle-related data.
The Netherlands-based chip maker said the collaboration with AWS will create new service and revenue opportunities in the automotive industry, including around after-sales predictive maintenance and servicing and insurance incentives around vehicle usage and driving behaviour.
It said companies playing in the automotive space need “expanded access to real-time, vehicle-wide data”, along with “secure connectivity to cloud services and streamlined machine learning” in order to capitalise on the shift towards connected and autonomous vehicles.
NXP commented: “Real-time vehicle insights can drive a myriad of new use cases and services… But the automotive industry requires a new type of compute solution with centralized access to vehicle-wide data that can work securely and collaboratively with the cloud.”
The new NXP S32G processor leverages AWS IoT Greengrass and AWS IoT Core for vehicle and cloud data processing and storage, as well as Amazon SageMaker and Amazon SageMaker Neo to build, train and deploy optimized ML models. Deep learning inferencing in the vehicle is supported by NXP’s eIQ Auto toolkit.
It suggested a number of examples of future revenue streams, based on secure sharing of vehicle data, including: monitoring for component failures before repairs are needed, to prevent costly repairs and recalls, and to streamline supply chains and minimise service visits; monitoring driving and usage to offer reduced insurance premiums; and introducing new sensors to create new in-vehicle safety and security features.
Henri Ardevol, executive vice president and general manager of automotive processing at NXP, said: “NXP is pleased to collaborate with AWS to help our automotive customers accelerate their digital transformation initiatives by unlocking the value of vehicle data and leveraging edge-to-cloud services. We see the opportunity to help them make impactful improvements throughout vehicle life cycles with new vehicle data insights and the ability to make continuous improvements using machine learning and over-the-air updates.”
