Self-Driving Cars Will Be Confounded By Lopsided Traffic Mixtures

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/]

Car traffic can be downright exasperating, frustrating, beguiling, exhausting, and just a real pain in the neck.

Most of us dread getting stuck in traffic.

Endless sea of cars.

Stop and go movement.

Bumper to bumper with a chance of some scrapes and fender benders.

Traffic Is A Delicate Dance Of Cars And Car Driving

Generally, our existing laws and rules-of-the-road allow for human drivers to exercise a certain amount of discretion within loosely bounded legal rules.

When a car to my right suddenly jams into my lane, and fails to signal, and fails to wait until there’s a reasonable opening, and fails to clear my car by more than a fraction of an inch, you could say that they have violated the law by creating an unsafe driving situation. But, who’s going to give them a ticket or stop them from this kind of discretionary driving? Unless by a stroke of luck there’s a traffic cop there, this kind of driving behavior is going to be gotten away with, unfortunately.

Thus, the day-to-day traffic that we survive in can be characterized as not being uniform, and not necessarily enforceable as to the strict interpretation of the laws of driving, and overall allows for human judgement to be used to decide in what way someone will drive their car.

If the roadway is pretty wide open and there are lots of lanes to choose among, the probability of a wayward discretionary driver that wanders up toward the legal outlaw line and getting caught is relatively low, while once there is a significant amount of traffic the probability generally gets higher. Also, once there is a significant amount of traffic, we now have more cars to contend with, and thus if say only 10% of drivers are the outlaw types, when you have just 10 cars nearby that means there is only 1 driver of the wayward type, while if you have 50 cars then you have 5 that are entered into the mix. In essence, volume of traffic makes a difference.

With the volume of traffic, we also need to consider density.

Generally, the more volume and the higher the density of traffic, the more that the actions of one driver can impact the other cars and drivers.

We could say that this is perhaps due to the magnitude of coupling.

Cars that have lots of room around themselves to maneuver can be considered more loosely coupled to those other cars around them. When the space between cars is tightened, it tends to increase coupling. I am referring to a virtual kind of coupling, and not any kind of true physical connections. It’s as though we had virtual invisible elastic bands that are connecting the nearby cars, and so when there is plenty of room then the cars are remotely coupled, and when they are closer to each other they are more directly coupled.

In close quarters traffic, if you suddenly get in front of my car by switching lanes without warning, and you do so within feet of my front bumper, I am likely to touch on my brakes to give you some added room and try to ensure that I don’t ram into the back of your car. Meanwhile, the car behind me, which we’ll say is also in close quarters, they too now might need to touch on their brakes. And so on it goes. A cascading effort can occur. It’s like a bunch of dominoes lined up.

AI Autonomous Cars And Traffic Mix

What does this discussion about traffic mix have to do with AI self-driving driverless autonomous cars?

At the Cybernetic Self-Driving Car Institute, we are developing AI systems for self-driving cars and have been studying extensively traffic mix and the nature of self-driving car driving techniques and approaches.

Some AI pundits claim that there’s no need to study traffic mix since the world will be a wondrous place once we have all self-driving cars on the roadways.

In this Utopia, the self-driving cars will all communicate with each other via V2V (vehicle-to-vehicle communication), and politely share the roads with each other.

This might someday be our future.

But, until then, the real truth of the matter is that we’re going to have a mixture of AI self-driving cars and human driven cars. I say this because right now in the United States alone we have about 200+ million conventional cars. Those conventional cars aren’t going to disappear overnight.

AI pundits complain that if humans still insist on driving a car, it’s going to mess things up. In one sense, they might be right. Based on studying the nature of traffic mixes, we can simulate what the future might be like in terms of the mix of human driven cars and AI self-driving cars.

Let’s first start by thinking about proportions related to the mix:

  • 1:N — this is one AI self-driving car that is in the midst of N human driven cars
  • N:1 — this is N number of AI self-driving cars that are in the midst of one human driven car
  • 10%:N — this is a ten percentage mix of AI self-driving cars in a volume of cars that includes N human driven cars
  • 30%:70% — this is a circumstance wherein the volume of cars has 30% that are AI self-driving cars and has 70% that are human driven cars
  • Etc.

I’ll be using this above nomenclature when referring to the various mixtures in traffic of AI self-driving car and human driven cars.

Let’s also agree that when I refer to a volume of traffic, it is with respect to a given circumstance.

I am also going to for now make the assumption that we have a relatively high density of traffic in these circumstances and the volume is relatively high too. I mention this for the same reasons that I had earlier stated that when the traffic is wide open, the nature of how the traffic intermixes is generally different than the circumstances when there is tighter coupling.

Additional Factors To Be Considered

There are more factors to be considered too about the traffic mix situation.

One really vital question involves how will human drivers react to AI self-driving cars?

You might at first say that human drivers won’t react any differently to an AI self-driving car than they do to another human driven car.

Well, you’d be wrong.

Human drivers will definitely be reacting differently to AI self-driving cars than they do to human driven cars, at least for the foreseeable future.

We’ve already seen that when AI self-driving cars are among conventional human traffic, the human drivers tend to give the AI self-driving car wide berth.

Most of driverless cars are driving very slowly and cautiously. They come to a full stop at stop signs. They go less than the speed limit in places that most human drivers exceed the speed limit.

This is a crucial factor when creating a simulation.

Most of the traffic mix simulations assume that the human drivers will be unaware that they are driving with AI self-driving cars around them. The simulations also assume that the AI self-driving car will drive in the same manner that humans drive cars, such as speeding, cutting corners, and so on.

Or, worse still, the simulations assume that all drivers will all abide strictly by the rules of driving and be polite and respectful, regardless whether a human driver or an AI self-driving car. I think we can agree that human drivers don’t drive that way.

Therefore, a realistic traffic mix simulation needs to consider for now that:

  • Human drivers will drive as human drivers do, exploiting their allowed latitude and being wayward
  • AI self-driving cars for the foreseeable future will drive in a more limited novice manner and be hardly wayward at all
  • Human drivers will drive differently upon detecting that an AI self-driving car is nearby and will re-actively drive because of the AI self-driving car being in their midst

Traffic Mix Proportions

Let’s now return to the traffic mix proportions.

An interesting research study seems to suggest that having even one AI self-driving car, equipped with V2V, could improve safety and save energy in traffic (a study at the University of Michigan, “Experimental Validation of Connected Automated Vehicle Design Among Human-Driven Vehicles,” partially funded by Mcity).

I applaud the researchers for their efforts.

Not only did they do simulated aspects, they also ran a series of experiments on public roadways with actual cars, including AI self-driving cars and human driven cars.

This is the kind of work needed to help advance the AI self-driving car emergence.

In the experiment, the researchers were exploring what happens in a chain of cars when there is a cascading impact or chain-reaction due to a car braking and then re-accelerating. They found that the self-driving car was able to more smoothly deal with the circumstance, braking with 60% less of the G-forces and improving energy efficiency by 19%. The humans involved in the driving experiment were acting as a typical human driver might, namely tending to brake hard when caught by surprise about the chain reaction and then having to do a more rapid re-acceleration too.

Part of the trick here in this experiment is the V2V aspects.

This is great, but it also is focused on the future of when we’ll actually have widespread V2V.

Until then, we’re going to have AI self-driving cars on the roadways that either lack V2V, or are outfitted with V2V but no other cars anywhere near them also have V2V.

Also, as stated in their research, they were focused on single lane types of driving, and more expansive studies are needed to look at a fuller mix of traffic including multi-lane situations.

In our simulations, using the aforementioned assumptions about driver behaviors, we’ve found that when you have the situation of 1:N, this tends to actually worsen the traffic situation.

When there is a sole AI self-driving car among many human driven cars, it’s the equivalent of having a novice teenage driver among many seasoned drivers. The novice driver tends to go slowly and react timidly, which then causes the seasoned drivers to become provoked and try to find ways around that driver. It’s like a stream of water that a rock has been tossed into. The rest of the stream tries to find ways to get around that car. This is something to keep in mind in these early days of the adoption of AI self-driving cars on our public roadways.

In a similar kind of result, there’s the N:1.

When you have essentially all AI self-driving cars and mix into it just one human driven car, it tends to disrupt the traffic. This is because the wily human driver tends to drive in a wayward fashion, while the AI self-driving cars are all trying to work cooperatively and in coordination with each other.

Now, I would suggest that we’re not going to see anytime soon an entire array of AI self-driving cars and one lone human driven car mixed together.

By the time that happens, I’m betting that the AI self-driving cars will have been better equipped and programmed to handle the wayward human drivers and so they will be adept enough to cope with the human driver in their midst. In essence, I’m suggesting that by the time there is only a lone human driver among lots of AI self-driving cars, we will likely have first had a more proportionate mix of AI self-driving cars and human driven cars, and when the last few holdout human drivers are around will we have the N:1 circumstance (they’ll have to pry the steering wheel from their cold hard hands, so to speak).

Until the proportion of AI self-driving cars gets high enough and reaches a threshold, the human driven cars are still generally operating as humans do. With enough of the AI self-driving cars and once they get sophisticated enough, they are able to contend with those disruptive drivers. The disruptive drivers eventually too figure out that the AI self-driving cars are wise to them. Up until that point, the human drivers figure they’ll pull the wool over the eyes of the AI self-driving cars and treat them like patsies that are readily exploitable.

Another aspect that we include in our simulations is the impact of other driving aspects such as the mix of having motorcyclists, pedestrians, bicyclists, and other traffic elements.

Some studies focus on freeway only traffic situations, which then cuts out pedestrians, bicyclists, and other city or street regular driving circumstances. Some AI pundits say that we should have freeways devoted solely to AI self-driving cars, or if that’s not feasible then at least have lanes dedicated to AI self-driving cars. The concept there is that we might be able to gain the advantages of the all-and-only AI self-driving cars by giving them their own place to drive. This though will require some potential hefty changes in our roadway infrastructure.

Conclusion

Today’s traffic can be maddening.

Imagine in the future when you get stuck in bumper to bumper traffic and look at the car next to you and there’s no one driving the car.

Will you still be able to show your finger to that non-human self-driving car? Even if you can, will it make a difference?

In whatever manner this all plays out, I think we can assume that we’ll have a small proportion of AI self-driving cars at first, which will gradually grow over time. At each of these stages of evolution of our traffic mix, we’ll see somewhat different traffic patterns emerge.

This is crucial to keep in mind when planning how we’ll be dealing with the mixing together of human drivers and AI self-driving cars.

Will it be like oil and water?

Or, can we get it to be more like milk and cereal?

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 © 2019 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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