An Autonomous Vehicles Network with 5G URLLC Technology

For example, in fully automated driving with no human intervention, vehicles can benefit from the information received from roadside infrastructure or other vehicles, to conduct automated overtaking, cooperative collision avoidance or high-density platooning,. And in smart intersections, vehicles can co-ordinate with traffic lights and other systems so that emergency vehicles can be prioritized as well as buses. All these applications all require a very high level of reliability and strict end-to-end latencies which only a URLLC communications network can guarantee.

However, to store and process the massive amount of data generated by intelligent vehicles, from their multitude of high resolution cameras and sensors (including RADAR, LIDAR, SONAR and GPUS etc.), as well as being able to achieve better safety than the best human driver by processing real-time traffic conditions within a latency of 100ms (the best human driver can take action within 100 ~ 150ms), using either onboard processing capabilities or cloud computing will not be sufficient.

The answer will be to deploy storage and computing resources at the wireless network edge, including edge caching, edge computing and edge AI, using a MEC ("Mobile Edge Computing" or "Multi-access Edge Computing") network architecture, running on Baseband Unit (BBU) servers at base stations or radio access points along the roadside. A cloud-native BBU server is fully software-defined and reconfigurable on demand, and can be adopted for both CRAN and DRAN installations. It can run Virtualized RAN (vRAN) services (with Network Functions Virtualization) together with MEC applications such autonomous driving, mapping and others deployed by the vehicle manufacturer, the traffic department or other third parties.

One of the most important requirements of URLLC network is reliability and redundancy. This is fulfilled on many levels, such as the software, application, networking, transmission and physical levels. A cloud-native BBU server virtualizes networking, routing, switching and security functions of the wireless radio network, ensuring a high level of redundancy and reliability since containers or virtual machines can easily be re-generated and hot standbys are always available in case of failure.

On the physical hardware level, GIGABYTE's H242 Series server features a multi-node design to ensure adequate physical redundancy – if one node fails, the virtualized environment immediately will shift the workload to the other physical nodes within the server, and the faulty node can be quickly and cheaply replaced for a new one.

Network slicing is an architecture that enables different virtualized and independent logical networks on the same physical network infrastructure, in order to meet diversified service requirements.

The fully cloud-native architecture of 5G and MEC is ideal to support network slicing, allowing for a URLLC network to run on the same physical infrastructure as other 5G services to save on infrastructure investment and network operating costs. Network slicing is the key to meeting 5G's diverse requirements, and is a perfect solution to seamlessly integrate an autonomous vehicles network together with other applications with different network service requirements such as for eMBB or mMTC.

The realization of URLLC can empower several technological transformations in the transportation industry, including automated driving, road safety and traffic efficiency services. These transformations will get cars fully connected such that they can react to increasingly complex road situations by cooperating with others rather than relying on their local information. These trends will require information to be disseminated among vehicles reliably within extremely short time duration. A MEC network architecture running together with vRAN services on GIGABYTE's edge servers such as the H242 Series therefore will be an ideal way to enable an URLLC 5G network for an intelligent Internet of Vehicles.