5G and self-driving
vehicles: The
next-generation
automotive industry

If we're ever going to have autonomous vehicles, 5G and connected cars will need to share the road. The self-driving vehicle can be more cost-efficient, safer and reliable by leveraging multi-access edge computing (MEC), enabled by the low latency of 5G.

Current 4G LTE network technologies can provide significant improvements in automotive safety by incorporating connected vehicle technology with connected infrastructure, pedestrians and much more using MEC processing capabilities. By migrating to 5G, latency can be reduced even further, providing increased safety margins and reliability. Getting to a point where self-driving cars are practically ubiquitous could dramatically improve safety, and the pairing of 5G and connected cars can create other benefits for drivers, passengers, communities and those who plan their growth and development.

Here's what the future of the automotive industry may look like.

While it's possible to enable autonomous driving without 5G, the widespread availability of low-latency 5G networks is what will make self-driving cars far more viable. Particularly when they're combined with the development of machine learning models running on MEC that can interpret traffic and artificial intelligence to emulate a smart, defensive driver. MEC can support these models by tapping into the data regarding the traffic surrounding the self-driving vehicle. 

Autonomous driving needs more than a system of roadways; it needs a network-based architecture that leverages the pairing of 5G and cars to meet key connectivity requirements.

A self-driving vehicle needs reliable networking that supports real-time communications and a whole host of sensors that function across different domains, as well as a high degree of redundancy. These smarter cars are destined to be highly sophisticated, further expanding on the existing Advanced Driver Assistance System (ADAS) with more improved, high-resolution sensors as well as human-machine interfaces. When 5G connects the car to the surrounding infrastructure, the vehicle has access to a high-speed data backbone and Multi-Access Edge Compute (MEC) processing.

The surrounding infrastructure is expected to include more sensors that generate and share data through a 5G network to enable an autonomous driving ecosystem. The car itself will be a computer, full of sensors, memory, storage and compute power that can handle multiple data streams at once. Some streams are essential for enabling autonomous driving, but others gather information to make traffic management systems and cities smarter, and provide rich infotainment for passengers.

All these data transfers will need to be secured. Inside the vehicle, wires will support in-vehicle networks using automotive Ethernet standards that include encryption. In the meantime, there will be an accelerated need for automotive-grade components, including miniaturized connectivity, as the number of sensors and internal communications links rises dramatically.

Internal connectivity must be supported by robust, external connectivity, which will be enabled by cellular vehicle-to-everything (V2X). This will enable the 5G car technology to interact with its immediate surroundings as it interprets data gathered from its environment using both sensor technology and cellular communication.

Autonomous vehicles must be capable of several different modes of communication, so they can safely and effectively interact with their surroundings. Some modes support safe, responsive driving while others enable different forms of data gathering and analytics with more long-term uses:

• Vehicle-to-vehicle (V2V) communications enable an autonomous vehicle to share information with other vehicles.
• Vehicle-to-pedestrian (V2P) transmissions relay a vehicle's intent and alerts to nearby pedestrians.
• Vehicle-to-infrastructure (V2I) communication allows the vehicle to receive critical information from Intelligent Traffic Systems (ITS).
• Vehicle-to-network (V2N) connects the vehicle to service providers and links a self-driving car to cloud applications with analytics functions that are able to process large amounts of raw data and derive meaningful, actionable information.

All of these modes contribute to fostering the need for rapid, low-latency communications to enable safe driving and address immediate traffic management demands.