Exploring the potential of automotive edge computing

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The rise of connected, autonomous, shared mobility and electric (CASE) vehicles is transforming the automotive industry as we know it. With cars becoming mobile datacenters equipped with sensors, Lidar, and cameras, the amount of data generated is immense. This is where automotive edge computing comes into play, addressing challenges related to data workload performance, latency and security. In this blog post we will delve into use cases of automotive workload computing and the impact of 5G integration, and highlight companies and contributors in this space.

Automotive workload computing

451 Research categorizes IT/compute venues into the following: on-premises edge, near edge/as-a-service edge, and core/cloud.

On-premises edge, on-device (in-vehicle): This form of automotive edge computing brings the data storage closer to where it is needed. Processing data directly within the vehicle enables critical safety tasks like forward collision warning and autonomous emergency braking systems. This approach minimizes latency, ensures swift response times and facilitates real-time processing. On-device computing also allows for localized decision-making, reducing the reliance on cloud connectivity.

Near edge/as-a-service edge (off-vehicle): This refers to computing infrastructure (i.e., datacenters) closer to the data source and users, which allows for faster response times and less reliance on distant datacenters. Near-edge compute is particularly relevant for applications like real-time traffic management and in-vehicle infotainment. It offers ultra-low latency, high bandwidth and real-time access to radio network information. By processing data closer to the source, near-edge computing reduces the need for extensive data transmission, resulting in improved efficiency and reduced network congestion.

Multi-access edge computing sits within this form of automotive edge computing, offering application developers and content providers cloud-computing capabilities and an IT service environment at the edge of the network, along with adjacent network connectivity.

Core/cloud (off-vehicle): This processing occurs at a distant location from the data source, such as regional datacenters or cloud venues, and is suitable for less-time-sensitive software, over-the-air updates and navigation systems. This outermost layer of computing provides scalability and network resilience. Core/cloud computing allows for centralized data storage, advanced analytics and complex computations that may require significant processing power.

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Impact of 5G integration

The integration of 5G in the automotive sector brings advantages like high-speed connectivity, low latency and improved mobile bandwidth. This enhances vehicle functionality, safety and user experience. 5G adoption is expected to boost the use of edge applications, prompting a shift in data workflows from on-device processing to the near edge or cloud. A hybrid approach combining edge computing and cloud processing can increase performance and manage costs. With 5G’s capabilities, autonomous vehicles can leverage real-time data from edge computing infrastructure to make informed decisions, helping improve safety and efficiency.

Automotive edge companies and contributions

Several companies are actively contributing to automotive edge computing. NVIDIA Corp. offers AI-powered driving and services, AI-assisted driving, and AI cockpit and infotainment. Qualcomm Inc. offers cellular vehicle-to-everything personalized experiences, advanced driver-assistance systems, and autonomous driving (AD.) Its Snapdragon Digital Chassis focuses on telematics and connectivity, digital cockpit and driver assistance, and autonomy. Bosch Mobility focuses on EV battery services, diagnostics, V2X and in-vehicle infotainment. Automotive battery data is prefiltered in the Bosch telematics control unit, sent to the cloud for analysis, and provides battery condition predictions to vehicles or smartphone apps. Harman provides V2X communications for safety-critical applications like hazard alerts, and high-throughput connectivity for interactive infotainment. Mobileye Global Inc. offers ADAS and AD solutions, including operational point-to-point AD navigation and cloud-based enhancements like road experience management.

Conclusion

To thrive in the era of automotive edge computing, a comprehensive and strategic approach is required. Digital infrastructure providers must assess the transformative implications of edge computing and the transition to decentralized architectures. This includes ensuring seamless communication between edge devices and central systems, addressing security challenges, and optimizing resource utilization. With the potential of 5G and advancements in edge computing technologies, the automotive industry is poised for innovative use cases and improved efficiency. By harnessing the power of edge computing, vehicles can process data in real time, enabling enhanced safety features, personalized experiences and optimized performance.

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