On The Difficulties Of Scheduling Self-Driving Cars To Coordinate Them Across Autonomous Fleets

Dr. Lance Eliot, AI Insider

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[Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/]

Norfolk Southern Corp is doing a makeover of some rather convoluted train yards.

By revamping the freight train operations, there are intentions to make this complicated dance into one that is tightly woven with specific entry and exit times, predicted in-advance, and carefully tracked schedules. Presumably, this will allow for more freight movement, more timely freight movement, and make better use of the railroad’s scarce resources.

Precision Scheduling Railroading (PSR)

The notion of transforming the freight train operations is being referred to as Precision Scheduling Railroading (PSR).

In theory, the PSR approach should be able to achieve the desired boosts in efficiency and effectiveness.

This effort also needs to consider the ramifications of upstream and downstream vital touch-points.

The hub itself has its own constraints too, that need to be considered.

Let’s assume that there are a set number of tracks, N, and you come up with a schedule that assumes there are N+1 tracks, well, that’s going to be a problem for those workers at the hub (not enough tracks to abide by the system produced schedule!).

Or, maybe the scheduling system assumes that all N tracks can be used, but it could be that on some days a given track has problems and needs to be repaired before it can be used, so there are really only N-1 or N-2, etc. available tracks. Does the scheduling system take that kind of contingency into account?

You are going to have some number of trains coming into the hub, a number T. Meanwhile, there are some number of trains trying to exit from the hub, a number X. Can those T coming into the hub do so on that number of tracks N, while at the same time dealing with the X number of trains aiming to get out of the hub on those same tracks N?

In today’s modern age, there are lots of excellent scheduling systems that are used for a variety of industries, and can handle these kinds of complexities, thus the train hub is not unique or an impossible operation to come under PSR. The point is that it is not as easy as it might seem at first glance. Switching over from an older set of processes to a new set can be tricky and slamming in a fancy scheduling system is not something you can do overnight.

I’d like to focus your attention on another kind of scheduling problem that will soon become a notable and visible concern.

It has to do with the advent of ridesharing.

Ridesharing as a Scheduling Problem

Similar to the freight trains, there are a multitude of needs for transport in ridesharing and a definite need to figure out the balance of supply and demand.

Unfortunately, an ad hoc method can be a hit-or-miss and instead, presumably, a well-design and well-implemented approach is likely to produce better results, once it has been put in place and tweaked accordingly.

Self-Driving Cars and Ridesharing Aspects

What does this have to do with AI self-driving driverless autonomous cars?

Most pundits predict that AI self-driving cars will be used as ridesharing cars, doing so to recoup their cost and earn some added dough by fully utilizing the self-driving cars. This will likely lead to some hefty scheduling issues.

I’ve stated in my writings and speeches that the advent of AI self-driving cars will be more so than solely being done as fleets.

As background for you, some pundits claim that no one will individually own an AI self-driving car because such vehicles will be overly expensive. Therefore, in this theory, AI self-driving cars will be owned by the likes of either automakers, tech firms, or ridesharing firms, and be considered as working in collectives that we might call a fleet of AI self-driving cars.

That seems like a rather narrow view of the future.

AI Self-Driving Cars to Become a Flood of Ridesharing

If an automaker or tech firm or ridesharing firm can make a buck off of AI self-driving cars, why wouldn’t individuals seek to do the same?

Those with this other theory are narrow thinking in that they view car ownership as simply and exclusively a cost. Today, when you buy a car, you use it to go to work, and going on vacation, and driving to the store, etc. You aren’t making money by owning the car. It is your means of conveniently getting around.

The advantage of an AI self-driving car is that it comes with a built-in driver (in the case of true Level 5 AI self-driving cars). This means that the AI self-driving car can be used whenever you want, and you don’t need to be the driver, and nor do you need to find or hire a driver. Keeping in mind too that most people only use their car for about 5–10% of the day, a car is a tremendously underutilized asset that can be deployed to your personal and financial well-being.

How could you afford an AI self-driving car if it might cost into the hundreds of thousands of dollars? Easy, by turning it into a money maker. While you are at work, you send your AI self-driving car out to do ridesharing. When you are asleep, you do likewise.

This will create a huge cottage industry of small businesses, whereby you purposely buy an AI self-driving car, likely taking out a loan to cover it, and are anticipating that the revenue generated by the AI self-driving car will make your purchase worthwhile. There is a chance of a solid ROI (Return On Investment) for this approach of buying an expensive asset and putting it to work.

That being said, I’ve also forewarned that this blossoming might get somewhat out-of-hand.

Besides individuals jumping into the fray, and besides the usual suspects like ridesharing firms and automakers, you might as well add other kinds of firms too. You could be a firm in a completely unrelated industry and see the writing on the wall that money can be made off the backs of AI self-driving cars.

Today’s firms that make money from the utilization of cars for ridesharing have to jump through lots of hoops to do so. Ridesharing firms need to find drivers and keep those drivers happy. No need to do so for an AI system that’s your always available driver, it’s happy already (well, kind of). You can also readily outsource things like the maintenance needed for the self-driving cars and other kinds of logistics aspects.

If my predictions come true, we’ll see a flood of AI self-driving cars that are flowing in and around our streets. This will be the next gold rush.

Let’s consider then the notion of AI self-driving cars roaming around our streets.

Pundits tend to imagine a Utopian world in which you come out to the street and within seconds there is an AI self-driving car there at your beck and call. Sounds great! We will all be able to reduce delay time in getting a ride. Rides on demand.

Yes, that might be true, but how did that AI self-driving car get to you, doing so quickly?

You might have requested it in-advance, perhaps via a mobile app, and then when it arrived, you went outside to get into it for your ride. That’s one way to arrange the ride.

Another involves simply going out to the curb and hailing a ride. I’d dare say most of us are using that method these days. You used to hail a cab by waving frantically at cabs that wandered past you. Now, you use your mobile app to see how far away a ride might be, and once you select it, the driver heads in your direction.

If you choose to use a particular ridesharing service, it means that you are only going to be seeing those available ridesharing cars that are perchance signed-up with that service. There might be other ridesharing services that have available cars and those are even closer to your position at the curb, at that moment, but you tend to ignore them and go with the ridesharing service that you prefer.

Suppose in the future that there are zillions of ridesharing cars that are nearby when you happen to go out to the curb. Rather than being focused on one particular ridesharing firm, you might be willing to go with whichever ridesharing car happens to get there soonest. Of course, you also care about the cost, and the quality of the ride, and let’s assume for the moment that’s a given.

Put on your hat of the firms and individuals that will own AI self-driving cars and are trying to make money by using those self-driving cars as a ridesharing service.

They want to put their AI self-driving car in places that will maximize their revenues of doing ridesharing. This means they want their AI self-driving car to be chosen for a paying fare. They also want to minimize the unused time of their AI self-driving car, which essentially is nonpaying, such as when their AI self-driving car is roaming to find a fare.

The Future of Ridesharing via AI Self-Driving Cars

Here’s what might happen.

All these businesses that have AI self-driving cars are going to want to funnel them into whatever places and at whatever times will earn them the most in fare revenues. Since they all want to do this, you’ll have a grand convergence of AI self-driving cars, all flocking to the same places, ones that seem to offer the most chance of getting riders.

For the human riders that want a ride, it could be a nirvana of choices. Those AI self-driving cars all coming to get you, and presumably the owners might have setup various special discounts and incentives. Use the XYZ ridesharing AI self-driving car that’s coming down the street right now, and you’ll get a 10% off for picking it, rather than using the ABC ridesharing AI self-driving car that’s this moment pulled to the curb where you are standing. Isn’t it worth the 10% off to wait another 15 seconds for your ride?

I’d like to also take a momentary step back and ask you to contemplate what this kind of flood of AI self-driving cars will do to the traffic situation.

If you have a belly full of AI self-driving cars trying to all circle around and around among a few blocks area in downtown, the resultant impact to traffic movement will be startling. Gridlock will ensue. Those AI self-driving cars don’t care per se about sitting in traffic, which humans tend to avoid. The only thing to curtail the AI self-driving car from sitting in traffic is the opportunity lost of potentially snagging a fare, because the AI self-driving car was stuck in traffic a block from where a rider was seeking to get a ride.

Precision Scheduling of Autonomous and Human-Based Ridesharing (PSAHBR)

One solution to the AI self-driving car flood of ridesharing might be to consider putting in place a kind of universal Precision Scheduling of Autonomous and Human-Based Ridesharing (PSAHBR) system.

In essence, ridesharing services would put into inventory of this universal scheduling system their AI self-driving cars as an available ridesharing vehicle. The system would then try to schedule the placement of the AI self-driving cars to meet demand.

It will be a complicated algorithm, that’s for sure.

In a manner of speaking, it is reminiscent of the National Resident Matching Program (NRMP), often referred to as The Match, which occurs in the United States and involves the matching of U.S. medical school students into the available residency programs at teaching hospitals each year. A non-profit non-governmental entity was set up to do this. If you aren’t aware of it, you might want to look online about the matter, and it uses a famous problem known as the “stable marriage problem” as an underlying way to find an algorithm to deal with the matching process.

The mighty PSAHBR would be a kind of matching that involves the pairing of those seeking a ride with an available ridesharing car. Notice that I did not say that it would necessarily be an AI self-driving ridesharing car that is only in the inventory of the PSAHBR system, since human-driven cars would presumably be included too.

In theory, the PSAHBR would smoothen out the traffic situation and aim to reduce the continual and somewhat wasteful aspects of ridesharing roaming, whether by human drivers or by AI self-driving cars. The system would need to have an indication of where riders tend to want rides, and by using Machine Learning and Deep Learning could try to predict when rides are needed, along with figuring out optimal ways to arrange for the ridesharing inventory to be available at the right places at the right times.

One question right away that one needs to ask involves whether those that own the ridesharing cars are going to voluntarily seek to use such a system. It all depends.

If the PSAHBR can do a good enough job of scheduling, it would imply that the owners of the ridesharing services will earn more revenue and have less cost than if they had tried to just let their ridesharing cars roam. Obviously, the owners would be making a decision about whether it is better to go free or to use the system.

That being the case, there might be localities that decide to force the ridesharing services to use such a system. Akin to my earlier indication about airports, the airport authority is able to ban ridesharing from freely entering into the airport and force the ridesharing services to comply to the rules that are established. Presumably, a city could do likewise.

Who would put in place the PSAHBR?

It could be a non-profit non-governmental entity that was established to create and keep in shape such a system.

Or, it could be a governmental agency that opts to do so.

One would certainly expect that the major ridesharing services would be tending to craft something like this anyway, if nothing else to try and watch over their own fleets. Would other fleets join in?

Would the mom-and-pop cottage industry join in?

Likely it would depend upon the perceived “fairness” of how the ridesharing cars are given fares.

Conclusion

Whatever does happen in the future, I think it is a reasonable bet that once AI self-driving cars become prevalent, there will be a swirling of ridesharing that will make our heads spin. At first, it might seem like a welcomed capability. After things turn ugly due to the over abundance of ridesharing, there will be a wringing of the hands about what to do.

The public and the regulators are likely to realize that something needs to be done, once traffic snarls emerge and there is a cutthroat vying for fares.

Can someone get all of the ridesharing services to voluntarily come together into a universal scheduling system, or will it require a more heavy hand to do so?

Time will tell.

Meanwhile, for those of you that are interested in developing new and innovative apps, consider the kind of scheduling system that the PSAHBR would be, and get coding.

For free podcast of this story, visit: http://ai-selfdriving-cars.libsyn.com/website

The podcasts are also available on Spotify, iTunes, iHeartRadio, etc.

More info about AI self-driving cars, see: www.ai-selfdriving-cars.guru

To follow Lance Eliot on Twitter: https://twitter.com/@LanceEliot

For his Forbes.com blog, see: https://forbes.com/sites/lanceeliot/

For his AI Trends blog, see: www.aitrends.com/ai-insider/

For his Medium blog, see: https://medium.com/@lance.eliot

For Dr. Eliot’s books, see: https://www.amazon.com/author/lanceeliot

Copyright © 2020 Dr. Lance B. Eliot

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Dr. Lance B. Eliot is a renowned global expert on AI, Stanford Fellow at Stanford University, was a professor at USC, headed an AI Lab, top exec at a major VC.

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