Category Archives: Automation

Helping autonomous vehicles and humans share the road

Jeffrey C. Peters, Stanford University

A common fantasy for transportation enthusiasts and technology optimists is for self-driving cars and trucks to form the basis of a safe, streamlined, almost choreographed dance. In this dream, every vehicle – and cyclist and pedestrian – proceeds unimpeded on any route, as the rest of the traffic skillfully avoids collisions and even eliminates stop-and-go traffic. It’s a lot like the synchronized traffic chaos in “Rush Hour,” a short movie by Black Sheep Films.

‘Rush Hour’ by Black Sheep Films.

Today, autonomous cars are becoming more common, but safety is still a question. More than 30,000 people die on U.S. roads every year – nearly 100 a day. That’s despite the best efforts of government regulators, car manufacturers and human drivers alike. Early statistics from autonomous driving suggest that widespread automation could drive the death toll down significantly.

There’s a key problem, though: Computers like rules – solid, hard-and-fast instructions to follow. How should we program them to handle difficult situations? The hypotheticals are countless: What if the car has to choose between hitting one cyclist or five pedestrians? What if the car must decide to crash into a wall and kill its occupant, or slam through a group of kindergartners? How do we decide? Who does the deciding?

So far, our transportation system has evolved to be operated by humans, who are good at following guidelines but often interpret them to properly handle ambiguity. We stop midblock and wave a pedestrian across, even though there’s no crosswalk. We cross the double yellow line to leave cyclists enough room on the shoulder.

Improving our transportation system to take advantage of the best of machines and humans alike will require melding ambiguity and rigid rules. It will require creating rules that are, in certain ways, even more complex than what we have today. But in other ways it will need to be simpler. The system will not only have to allow automated drivers to function well: It must be easily and clearly understood by the humans at its center.

Human decision-making

Google cars, Uber self-driving cars, autonomous taxis in Singapore, Tesla’s autonomous mode and even self-driving freight trucks are already on the road. Despite one fatal crash – of a Tesla on autopilot – autonomous vehicles are still safer than a normal human driver. Nevertheless, that crash attracted a lot of media attention.

Among the roughly 100 deaths a day on U.S. roads, this one stood out because people wondered: If the driver was not relying on the autonomous software, what would have happened? What might the human have done differently?

That specific fatal crash was actually fairly straightforward: The car didn’t see a truck in front of it and drove into it. But when people think about accidents, they often worry about having to make moral choices in an instant.

Philosophers call this the “trolley problem,” after a hypothetical example in which a trolley is hurtling down a track toward some people who cannot get out of the way in time. You have the option to switch the trolley onto a different track, where it will hit some other people.

Switch the trolley, or don’t?
McGeddon, CC BY-SA

There are an infinite number of variations on the problem, created by specifying the numbers and types of people, replacing them with animals, sending the trolley into a wall where its passengers die, and more. Would you, for example, save five children and let a senior citizen die? What about saving a dog versus killing a criminal? You can try out many of these variations – and make new ones – online in a fascinating “Moral Machine” game from which MIT researchers are gathering information on what decisions people make. They hope to find at least some human moral consensus, which can then inform autonomous vehicles and other intelligent machines.

The crux of the problem is whether you choose to switch the trolley or not. In one case, you make an active decision to intervene, deciding to save – and kill – certain groups. In the other, you choose not to act, effectively letting fate take its course. People who use the Moral Machine can see how their results compare to everyone else’s. So far the outcomes suggest that people intervene to save younger, fitter people with higher perceived social values (doctors over criminals, for example).

Human – and computer – preferences

To handle these relative preferences, we could equip people with beacons on their cellphones to signal nearby cars that they are a certain type of person (child, elderly, pedestrian, cyclist). Then programmers could instruct their autonomous systems to make decisions based on priorities from surveys or experiments like the Moral Machine.

But that raises serious problems. For example, would an autonomous car that noticed a child running in the middle of traffic decide to run over your grandmother on the sidewalk instead?

What should an autonomous car do here?
Kids on bikes via

And what about groups of people? The Moral Machine’s creators and other researchers found that society as a whole has a strong preference for choosing to save more people. What if a negligent group of runners steered a car into your path while you walked alone?

The same study also showed that people would be less willing to purchase a vehicle that could include sacrificing the driver (themselves) as an option. If society as a whole is to benefit from the advantages of autonomous vehicles, we need people to buy the cars – so we need to make them more attractive to buyers. That might mean requiring cars to save drivers, as Mercedes has already decided to do.

Breaking the rules

Investigating the trolley problem reveals that “optimizing” for countless specific, but hypothetical, scenarios is not the solution. Further, if we allow autonomous vehicles to break the rules sometimes, under certain circumstances, perhaps malicious humans could game the system. For instance, a pedestrian could walk out in front of traffic without getting hit, but forcing cars to slam on the brakes. That one person might even cause multiple collisions, causing disruption without great risk to the disruptor.

Volvo has already noticed that some human drivers behave like bullies around autonomous cars. For example, a person might cut off an autonomous vehicle because he is confident the other car will avoid a collision itself. As a result, Volvo will not follow the currently common practice of clearly labeling autonomous cars on public roads. At least some of its test vehicles will remain unmarked, in hopes of measuring differences in human drivers’ behavior.

The Mercedes and Volvo developments are the first steps toward trying to clarify human expectations about autonomous cars. By standardizing people’s perceptions, it will be easier to predict what humans will do in different scenarios. That will help us engineer ways to keep everyone driving in harmony.

A common set of rules for all autonomous vehicles – whatever those are – will allow people to predict the cars’ behavior and adjust our behavior, policy and transportation infrastructure accordingly.

And if we’re going to make clearer rules, perhaps humans should follow them more closely too, as pedestrians, cyclists and drivers. In that world, we probably won’t find the perfect chaos of the “Rush Hour” short film. But it will be much more orderly – and safe and efficient – than today.

Jeffrey C. Peters, Postdoctoral Fellow in Studying Complex Systems, Stanford University

This article was originally published on The Conversation. Read the original article.

How maths and driverless cars could spell the end of traffic jams

Lorna Wilson, University of Bath

Being stuck in miles of halted traffic is not a relaxing way to start or finish a summer holiday. And as we crawl along the road, our views blocked by by slow-moving roofboxes and caravans, many of us will fantasise about a future free of traffic jams.

As a mathematician and motorist, I view traffic as a complex system, consisting of many interacting agents including cars, lorries, cyclists and pedestrians. Sometimes these agents interact in a free-flowing way and at other (infuriating) times they simply grind to a halt. All scenarios can be examined – and hopefully improved – using mathematical modelling, a way of describing the world in the language of maths.

Mathematical models tell us for instance that if drivers kept within the variable speed limits sometimes displayed on a motorway, traffic would flow consistently at, say, 50mph. Instead we tend to drive more aggressively, accelerating as soon as the opportunity arises – and being forced to brake moments later. The result is greater fuel consumption and a longer overall journey time. Cooperative driving seems to go against human nature when we get behind the wheel. But could this change if our roads were taken over by driverless cars?

Incorporating driverless cars into mathematical traffic models will prove key to improving traffic flow and assessing the various conditions in which traffic reaches a traffic jam threshold, or “jamming density”. The chances of reaching this point are affected by changes such as road layout, traffic volume and traffic light systems. And crucially, they are affected by whoever is in control of the vehicles.

In mathematical analysis, dense traffic can be treated as a flow and modelled using differential equations which describe the movement of fluids. Queuing models consider individual vehicles on a network of roads and the expected time they spend both in motion and waiting at junctions.

Another type of model consists of a grid in which cars’ positions are updated, according to certain rules, from one grid cell to the next. These rules can be based on their current velocity, acceleration and deceleration due to other vehicles and random events. This random deceleration is included to account for situations caused by something other than other vehicles – a pedestrian crossing the road for example, or a driver distracted by a passenger.

Adaptations to such models can take into account factors such as traffic light synchronisation or road closures, and they will need to be adapted further to take into account the movement of driverless cars.

In theory, autonomous cars will typically drive within the speed limits, have faster reaction times allowing them to drive closer together and will behave less randomly than humans, who tend to overreact in certain situations. On a tactical level, choosing the optimum route, accounting for obstacles and traffic density, driverless cars will behave in a more rational way, as they can communicate with other cars and quickly change route or driving behaviour.

It all adds up

So driverless cars may well make the mathematician’s job easier. Randomness is often introduced into models in order to incorporate unpredictable human behaviour. A system of driverless cars should be simpler to model than the equivalent human-driven traffic because there is less uncertainty. We could predict exactly how individual vehicles respond to events.

In a world with only driverless cars on the roads, computers would have full control of traffic. But for the time being, to avoid traffic jams we need to understand how autonomous and human-driven vehicles will interact together.

Of course, even with the best modelling, cooperative behaviour from driverless cars is not guaranteed. Different manufacturers might compete to come up with the best traffic-controlling software to ensure their cars get from A to B faster than their rivals. And, like the behaviour of individual human drivers, this could negatively affect everyone’s journey time.

But even supposing we managed to implement rules that optimised traffic flow for everyone, we could still get to the point where there are simply too many cars on the road, and jamming density is reached. Yet there is still potential for self-driving cars to help in this scenario.

Are we nearly at a mathematical solution yet?

Some car makers expect that eventually we will stop viewing cars as possessions and instead simply treat them as a transport service. Again, by applying mathematical techniques and modelling, we could optimise how this shared autonomous vehicle service could operate most efficiently, reducing the overall number of cars on the road. So while driverless cars alone might not rid us of traffic jams completely by themselves, an injection of mathematics into future policy could help navigate a smoother journey ahead.

Lorna Wilson, Commercial Research Associate, University of Bath

This article was originally published on The Conversation. Read the original article.

Driverless cars will change the way we think of car ownership

Hussein Dia, Swinburne University of Technology

The transition to fully driverless cars is still several years away, but vehicle automation has already started to change the way we are thinking about transportation, and it is set to disrupt business models throughout the automotive industry.

Driverless cars are also likely to create new business opportunities and have a broad reach, touching companies and industries beyond the automotive industry and giving rise to a wide range of products and services.

The introduction of autonomous driving technology will be gradual.
Mojomotors, Author provided

New business models

We currently have Uber developing a driverless vehicle, and Google advancing its driverless car and investigating a ridesharing model.

Meanwhile, Apple is reportedly gearing up to challenge Telsa in electric cars and Silicon Valley is extending its reach into the auto industry.

These developments signal the creation of an entirely new shared economy businesses that will tap into a new market that could see smart mobility seamlessly integrated in our lives.

Consider, for example, the opportunity to provide mobility as a service using shared on-demand driverless vehicle fleets. Research by Deloitte shows that car ownership is increasingly making less sense to many people, especially in urban areas.

Individuals are finding it difficult to justify tying up capital in an under-utilised asset that stays idle for 20 to 22 hours every day. Driverless on-demand shared vehicles provide a sensible option as a second car for many people and as the trend becomes more widespread, it may also begin to challenge the first car.

Results from a recent study by the International Transport Forum that modelled the impacts of shared driverless vehicle fleets for the city of Lisbon in Portugal demonstrates the impacts. It showed that the city’s mobility needs can be delivered with only 35% of vehicles during peak hours, when using shared driverless vehicles complementing high capacity rail. Over 24 hours, the city would need only 10% of the existing cars to meet its transportation needs.

The Lisbon study also found that while the overall volume of car travel would likely increase (because the vehicles will need to re-position after they drop off passengers), the driverless vehicles could still be turned into a major positive in the fight against air pollution if they were all-electric.

It also found that a shared self-driving fleet that replaces cars and buses is also likely to remove the need for all on-street parking, freeing an area equivalent to 210 soccer fields, or almost 20% of the total kerb-to-kerb street space.

Other studies have also shown that dynamic ridesharing using driverless vehicles will increase vehicle utilisation up to eight hours per day.

Car insurance

A recent study by McKinsey on disruptive technologies suggests that up to 90% of all accidents could be prevented by driverless vehicles. So why buy insurance if automation makes accidents far less likely?

“The truth is, if it’s a safer way of driving, it’s good for society and it’s bad for our insurance business,” the US business magnate Warren Buffet said recently when asked about the impact driverless vehicles may have on his GEICO car insurance subsidiary.

“Anything that cuts accidents by 30%, 40%, 50% would be wonderful, but we won’t be holding a party at our insurance company.”

Other studies have speculated that premiums could be reduced by 75%, especially if drivers are no longer required to get coverage, and liability is shifted from drivers to manufacturers and technology companies.

Under this scenario, insurers might move away from covering private customers from risk tied to “human error” to covering manufacturers and mobility providers against technical failure.

A Rand Corporation report also predicts that drivers might end up covering themselves with health insurance instead of vehicle insurance.

Will driverless vehicles destroy the very idea of ownership?

Does all this mean car ownership is passé? In some ways, you may not own every facet of your driveless car anyway. Vehicle manufacturers are arguing that since they own the software that runs a connected vehicle, they also own the machine that runs that program.

In comments submitted to the US Copyright Office, vehicle manufacturers argue that purchasers are only licensing the product and it would be unsafe for them to modify the vehicle programming or even make a repair. The Copyright Office is currently holding a hearing on the issue. If it rules in favour of the manufacturers, it will set a precedent that can change the whole landscape of vehicle ownership.

Not everyone will be excited by this vision, and many would be sceptical and disagree that we are at the cusp of a transformation in mobility. Others still want to drive and not everyone is likely to want to rideshare on a daily basis. Many might also argue that better investment in public transport would achieve similar outcomes.

Whether you embrace or object to these scenarios, the reality is driverless vehicles are coming and they will have socio-economic impacts and other effects on our society – some good and some bad.

I see them, along with urban transport technologies, as having a role in delivering new mobility solutions as part of a holistic approach to improve road safety and promote low carbon mobility. The market will ultimately determine whether they can succeed.

Hussein Dia, Associate professor, Swinburne University of Technology

This article was originally published on The Conversation. Read the original article.