BoldIQ Team Driverless cars are cool, but the network layer that will run them is even cooler – a BoldIQ byline in Venture Beat
Google has shown us that autonomous, driverless cars are now a reality, at least from a technological perspective. These vehicles are capable of reacting to unforeseen disruptions such as jaywalkers, roving bicyclists, or slowing traffic.
It is exciting and scary to think of the world ten years from now replete with these technological advancements. Our world will need to adjust.
Consider the air, where unmanned aerial vehicles or drones are already being used – and where a fierce battle is being waged between the FAA and prospective and current drone users. Concerns around safety, security, and more are all part of the huge debate in one of the more controlled environments we have: our airspace.
If this is what is happening in aviation, can you imagine what will happen when “autobots” hit our streets?
The environment of ground transportation is much larger and more dynamic than aviation, yet it has significantly fewer (if any) regulations in place. Drivers can go anywhere using any route they chose. Travel plans are not known in advance; and speed, while supposedly controlled by rules of the road, is often unpredictable and inconsistent.
All this causes near-collisions every day with licensed and trained humans behind the wheel. Imagine what will happen when we throw driverless vehicles into the mix. This is a highly complex dynamic network that requires real-time tools to manage it in order to be safe and efficient.
To do this, there are two layers of the network that should be addressed, combining both safety and efficiency. The first layer is the individual vehicles themselves. Google is doing a phenomenal job addressing this layer. The second layer is what I will call the network layer — the fleets of unmanned vehicles in aggregate. If we plan and execute the network layer correctly and in an adaptive fashion, we will actually need fewer vehicles (human-driven or driverless) to meet the demands of our day-to-day lives, whether that entails deliveries or public transportation.
And with fewer vehicles on the road, there is less of a safety risk at the individual vehicle level and the added benefit of less traffic, congestion, and waste. The ripple effects of this can, of course, be extended to a price reduction for deliveries and public transportation, etc.
This network needs to be structured yet rapidly adoptable and adaptable to meet the needs of the public, from both transportation and safety aspects. It is one thing for a single vehicle to sense a jaywalker and apply the brakes. It is another for the network to adapt itself in real-time to the butterfly effect of that car braking: the other cars braking; the human-driven car tailgating and not braking in time and colliding into the autonomous vehicle in front of it; the traffic jam created due to this incident; the ensuing delays; the delivery company needing to adjust its plans to meet its same-day delivery commitments; and the public transport authority needing to get the commuters to work on time.
As we move steadily and surely into an on-demand world, one which blends human and artifcially intelligent resources, we must think beyond a single resource, a single delivery or bus route. Using already available big data to streamline operations in real time is a highly effective way to manage a constantly changing environment, all while using the vehicles to the best of their capabilities. Optimizing real-time data streamed in from the vehicles can make responding and adjusting their usage in the real world possible, and it can give the public and regulators some of the comfort needed as we explore the possibilities.