Driverless Cars Hit the Streets — How Do We Prepare for the Network Effect? – a BoldIQ byline in Wired.com
Remember the 2054-model Lexus that Tom Cruise drove in the film “Minority Report”? Or who could forget Kit, the Knight Rider’s ultimate ride. Well it seems that Google is showing us that autonomous driverless cars are now a reality… at least from a technological perspective.
And Google is not alone in this race as it seems many of the major auto manufacturers are putting in the effort to be the first to market with an “autobot.” If we consider some of the actions taken by the United Nations recently to enable the use of driverless vehicles on the roads of Europe, it seems that European car makers believe they might be ready to lead the way.
I wonder if auto manufacturers and regulators alike are looking beyond the single vehicle technologies. There is much more that needs to be considered beyond the ability of a vehicle to swerve away from a pedestrian, or brake before hitting the car in front of it.
We need to anticipate and be ready for a highly complex dynamic network of vehicles – fleets of unmanned vehicles in aggregate. If we plan and execute the network correctly, and in a dynamic fashion, we will in fact need fewer vehicles (be they manned or unmanned) to perform the demands of our day to day activities, be they deliveries, public transport, and the like. To that end, I propose regulators and auto manufacturers take some key factors into account:
Anticipate Dynamic Complexity and Unpredictability:
Consider aerospace and what is happening with UAVs (otherwise known as drones). These highly technological pilotless planes are the subject of a raging battle between the FAA and the prospective (and in some cases already operational) users. Legitimate concerns around safety, security and more, are all part of the debate going on right now. And all of this is in one of the most controlled environments around – airspace. Known flights and flight paths, constants speeds, overly conservative separations between aircraft, air traffic control, etc.
If this is what is happening in aviation — where the National Safety Council calculated the odds of dying in a plane as 1 in 7,178 for a lifetime — can you imagine what will happen in our streets where the odds of dying in a car crash are 1 in 98 for a lifetime?
The movement of vehicles on the roads are unpredictable and not very controllable even without adding disruptions into the mix. This drives a need for real-time dynamic decision-making technologies that will match the complexity and speed of this system.
Leverage the Network of Vehicles:
There are two layers of the network that should be addressed combining both safety and efficiency. The first layer is the individual vehicles themselves. I am confident that companies like Google and Mercedes-Benz are doing a phenomenal job addressing this layer. The second layer, the aggregation of vehicles be they a fleet of delivery trucks, a fleet of buses, or a fleet of on-demand taxis, is where the biggest impact will be on traffic, congestion, and waste. By leveraging the network using sophisticated optimization tools, the number of vehicles per fleet, and in aggregate, can be significantly reduced creating positive ripple effects throughout the operational, environmental, and financial aspects of this network.
Prepare to be Adaptable in Real-Time:
This network needs to be structured, and yet rapidly adoptable and adaptable to meet the needs of the public from both the efficiency aspect as well as the safety one. 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 behind it braking in succession; the manned car tailgating the unmanned car not braking in time and colliding into it; the traffic jam created due to this incident; the ensuing delays; the delivery service needing to adjust its plans to meet its same day food delivery commitments, or the bus service needing to somehow get commuters to work on time. Using adaptable and adoptable optimization software in real-time, will enable a transportation system that operates effectively and efficiently.
As we progress with great excitement and anticipation into an on-demand world, one which blends manned and autonomous resources to meet our needs, we must think bigger and wider than the single car, a single package or a single bus route. I look forward to the not-too-distant future in which optimizing real-time data being streamed in from the ‘autobots’ can make responding and adjusting their usage in the real-world possible, and give the public and regulators some of the comfort needed as we explore the realm of possible.