The following was written by Jyoti Sharma, Senior Manager of Network Planning in the Technology, Architecture, and Planning (TSA&P) team at Verizon. Sharma has over 20 years of industry experience serving in key technology and telecommunication roles, including wireless systems engineering, system performance, and new technology introduction. In this article, she examines why 5G and MEC are essential for the safe operation of autonomous vehicles.
Imagine self-driving cars zipping through the streets and fleets of buses being operated remotely through town. In the near and far off future, how we get around will be fueled by data, and it’s data that will bring to life connected and autonomous vehicles. Moreover, this massive influx of data requires advanced technologies that offer faster processing speeds and substantial compute power so not just a single vehicle can travel safely, but entire fleets as well – all communicating with each other simultaneously.
This past June, California approved a pilot program for driverless rides, officially granting permission to Cruise, a self-driving car service based in San Francisco, to provide rides to passengers. This is one of many recent news stories that demonstrates just how we’re nearing the “future of transportation” depicted in science fiction. But the expectations on the vehicles of tomorrow are steep. Already we see vehicles as spaces for entertainment and consumption while keeping passengers and drivers safe. These functions generate and need massive amounts of data and processing power for video streams, coordinating positions with other vehicles, updates to travel based on real-time road and weather conditions, and much more.
Scaling an autonomous transportation system hinges on innumerable technologies working together. Yet three technologies – 5G, mobile edge computing (MEC), and hyper-precise location technology – are key to paving the way for the transportation ecosystem of the future. They ultimately form the network backbone that can process the wealth of data necessary to accelerate toward a future autonomous transportation system that is safer and more efficient.
5G & MEC Benefits
Data is at the core of autonomous vehicles and their evolution, and that’s where network capabilities become an essential part of the equation. Though connected and autonomous vehicles can currently run on 4G networks, 5G and MEC make them more viable. And combined with the development of machine learning models that can interpret traffic and artificial intelligence that emulates human drivers, greater adoption can be achieved.
The key to why 5G and MEC are essential to the adoption of driverless mobility is understanding that currently connected and autonomous vehicles are limited by a vehicle’s local processing and storage capabilities. The full scalability of autonomous vehicles requires the computing power and memory necessary to process external resources, such as city traffic management systems and weather and road condition data, all in near real-time and in concert.
MEC puts technology resources closer to the end-user so that data is processed and stored at the network’s edge versus primarily in the vehicle. Combined with the massive network bandwidth and speed of 5G, the onboard hardware and software requirements are lowered for autonomous vehicles without sacrificing the necessary speed and capabilities to make decisions as fast or faster than drivers. This could help lower auto manufacturer costs to produce driverless cars and the technology barriers limiting mainstream adoption.
Moreover, to enable the near real-time decision-making necessary for autonomous vehicles to become commonplace, the trip by data to-and-from the vehicle to other cars, road infrastructure, and anything it needs to communicate with to drive safely must be near-instantaneous. The 5G network’s low latency capability can significantly reduce lag, enabling mission-critical communications to happen in near-real-time, which is absolutely necessary for the quick judgment calls of autonomous and connected vehicles.
Hyper-Precise Location Data
As autonomous vehicles make their way to the mainstream car market, automakers and engineers are doubling down on road and driver safety. The key to connected vehicle safety is hyper-precise location technology that provides accuracy of one to two centimeters versus the accuracy level of three to nine meters with GPS alone.
Similar to how people scan the road with their eyes while driving, autonomous cars use sensors to monitor the road and will leverage networks equipped with hyper-precise location technology paired with collision avoidance apps that can precisely identify vehicles, pedestrians, bicycles, etc. Moreover, hyper-precise maps will go beyond predicting travel paths and road congestion to pinpointing which lane is optimal for a vehicle. Location accuracy to this degree can be helpful and necessary during inclement weather. For example, when fog or rain obscures perception sensors’ vision for autonomous vehicles.
Making it a Reality
As the auto industry advances connected cars and electric and autonomous vehicles, the 5G network combined with MEC and hyper-precise location technology could help accelerate and underpin further development. By lowering the burden of localized data processing and storage at the vehicle level with 5G and MEC, vehicle manufacturers can reduce automotive production costs.
With new technological advancements, vehicles can access the latest software and application updates via the mobile network – similar to when a smartphone receives operating system updates and new features and applications. And hyper-precise location technology can ultimately reduce the costs and risks associated with inaccurate location data while supporting critical safety technology for better navigation, smoother drives, and faster reactions to the surrounding environment.
With these technologies, combined with the growing support of consumers and government officials, we are well on our way to making the future of transportation a reality.
Jyoti Sharma is a Senior Manager of Network Planning in the Technology, Architecture and Planning (TSA&P) team at Verizon. Sharma is a senior member of IEEE and currently serves as the Chair of Women In Engineering at the IEEE North Jersey Section. She has worked as an Adjunct Professor at Fairleigh Dickinson University and DeVry University. Sharma earned a Ph.D. in Electrical Engineering from the Indian Institute of Technology, Delhi, India, a Master’s in Telecommunications from the Asian Institute of Technology, Bangkok, Thailand and a Bachelor’s in Electronics & Communications Engineering from the Delhi Institute of Technology, Delhi, India.