Speeding up traffic by 35 percent using driverless cars working together

via University of Cambridge

A fleet of driverless cars working together to keep traffic moving smoothly can improve overall traffic flow by at least 35 percent, researchers have shown.

For autonomous cars to be safely used on real roads, we need to know how they will interact with each other

Amanda Prorok

The researchers, from the University of Cambridge, programmed a small fleet of miniature robotic cars to drive on a multi-lane track and observed how the traffic flow changed when one of the cars stopped.

When the cars were not driving cooperatively, any cars behind the stopped car had to stop or slow down and wait for a gap in the traffic, as would typically happen on a real road. A queue quickly formed behind the stopped car and overall traffic flow was slowed.

However, when the cars were communicating with each other and driving cooperatively, as soon as one car stopped in the inner lane, it sent a signal to all the other cars. Cars in the outer lane that were in immediate proximity of the stopped car slowed down slightly so that cars in the inner lane were able to quickly pass the stopped car without having to stop or slow down significantly.

Additionally, when a human-controlled driver was put on the ‘road’ with the autonomous cars and moved around the track in an aggressive manner, the other cars were able to give way to avoid the aggressive driver, improving safety.

The results, to be presented today at the International Conference on Robotics and Automation (ICRA) in Montréal, will be useful for studying how autonomous cars can communicate with each other, and with cars controlled by human drivers, on real roads in the future.

“Autonomous cars could fix a lot of different problems associated with driving in cities, but there needs to be a way for them to work together,” said co-author Michael He, an undergraduate student at St John’s College, who designed the algorithms for the experiment.

“If different automotive manufacturers are all developing their own autonomous cars with their own software, those cars all need to communicate with each other effectively,” said co-author Nicholas Hyldmar, an undergraduate student at Downing College, who designed much of the hardware for the experiment.

The two students completed the work as part of an undergraduate research project in summer 2018, in the lab of Dr Amanda Prorok from Cambridge’s Department of Computer Science and Technology.

Many existing tests for multiple autonomous driverless cars are done digitally, or with scale models that are either too large or too expensive to carry out indoor experiments with fleets of cars.

Starting with inexpensive scale models of commercially-available vehicles with realistic steering systems, the Cambridge researchers adapted the cars with motion capture sensors and a Raspberry Pi, so that the cars could communicate via wifi.

They then adapted a lane-changing algorithm for autonomous cars to work with a fleet of cars. The original algorithm decides when a car should change lanes, based on whether it is safe to do so and whether changing lanes would help the car move through traffic more quickly. The adapted algorithm allows for cars to be packed more closely when changing lanes and adds a safety constraint to prevent crashes when speeds are low. A second algorithm allowed the cars to detect a projected car in front of it and make space.

They then tested the fleet in ‘egocentric’ and ‘cooperative’ driving modes, using both normal and aggressive driving behaviours, and observed how the fleet reacted to a stopped car. In the normal mode, cooperative driving improved traffic flow by 35% over egocentric driving, while for aggressive driving, the improvement was 45%. The researchers then tested how the fleet reacted to a single car controlled by a human via a joystick.

“Our design allows for a wide range of practical, low-cost experiments to be carried out on autonomous cars,” said Prorok. “For autonomous cars to be safely used on real roads, we need to know how they will interact with each other to improve safety and traffic flow.”

In future work, the researchers plan to use the fleet to test multi-car systems in more complex scenarios including roads with more lanes, intersections and a wider range of vehicle types.

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Connecting Vehicles

Visualization of an ORNL connected vehicles simulations using decentralized control algorithms developed by researchers with the lab’s Urban Dynamics Institute. Image provided by Andreas Malikopoulos.

Visualization of an ORNL connected vehicles simulations using decentralized control algorithms developed by researchers with the lab’s Urban Dynamics Institute. Image provided by Andreas Malikopoulos.

Computational framework for optimizing traffic flow could be the beginning of a road revolution

Drivers trying to get to work or home in a hurry know traffic congestion wastes a lot of time, but it also wastes a lot of fuel. In 2011, congestion caused people in US urban areas to travel an extra 5.5 billion hours and purchase an extra 2.9 billion gallons of fuel costing $121 billion. But despite the tangle of vehicles at busy intersections and interstate ramps, most of the country’s highways are open road with vehicles occupying only about 5 percent of road surface.

Scientists with the Urban Dynamics Institute (UDI) at the Department of Energy’s Oak Ridge National Laboratory are working to reduce travel time and fuel consumption by developing a computational framework for connected vehicle technologies that facilitate vehicle-to-vehicle communication, as well as communication between vehicles and traffic controls like traffic lights. Researchers envision vehicles exchanging information—such as location, speed, and destination—to generate individualized instructions for drivers. Watch a visualization of the process:

https://youtu.be/C32qUDqfgns.

“By telling drivers the optimal speed, the best lane to drive in, or the best route to take, we can eliminate stop-and-go driving and improve safety,” said Andreas Malikopoulos, UDI deputy director and principal investigator of the project. “As a driver, you may get additional instructions suggesting you change lanes or follow a different path that may not be the route your GPS would give you to avoid congestion.”

The first step for the project team is developing decentralized control algorithms that govern how vehicles will communicate locally among vehicles interacting directly on the road but also act globally to optimize traffic flow across a city. The computational framework uses “decentralized control” algorithms because, realistically, all the vehicles in a city cannot communicate information to a central control center due to the staggering amount of data that would be involved.

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Eliminating unexplained traffic jams

29-10-2013 4-26-57 PM

If integrated into adaptive cruise-control systems, a new algorithm could mitigate the type of freeway backup that seems to occur for no reason.

Everybody’s experienced it: a miserable backup on the freeway, which you think must be caused by an accident or construction, but which at some point thins out for no apparent reason.

Such “traffic flow instabilities” have been a subject of scientific study since the 1930s, but although there are a half-dozen different ways to mathematically model them, little has been done to prevent them.

At this month’s IEEE Conference on Intelligent Transport Systems, Berthold Horn, a professor in MIT’s Department of Electrical Engineering and Computer Science, presented a new algorithm for alleviating traffic flow instabilities, which he believes could be implemented by a variation of the adaptive cruise-control systems that are an option on many of today’s high-end cars.

A car with adaptive cruise control uses sensors, such as radar or laser rangefinders, to monitor the speed and distance of the car in front of it. That way, the driver doesn’t have to turn the cruise control off when traffic gets backed up: The car will automatically slow when it needs to and return to its programmed speed when possible.

Counterintuitively, a car equipped with Horn’s system would also use sensor information about the distance and velocity of the car behind it. A car that stays roughly halfway between those in front of it and behind it won’t have to slow down as precipitously if the car in front of it brakes; but it will also be less likely to pass on any unavoidable disruptions to the car behind it. Since the system looks in both directions at once, Horn describes it as “bilateral control.”

Traffic flow instabilities arise, Horn explains, because variations in velocity are magnified as they pass through a lane of traffic. “Suppose that you introduce a perturbation by just braking really hard for a moment, then that will propagate upstream and increase in amplitude as it goes away from you,” Horn says. “It’s kind of a chaotic system. It has positive feedback, and some little perturbation can get it going.”

Doing the math

Horn hit upon the notion of bilateral control after suffering through his own share of inexplicable backups on Massachusetts’ Interstate 93. Since he’s a computer scientist, he built a computer simulation to test it out.

The simulation seemed to bear out his intuition, but to publish, he needed mathematical proof. After a few false starts, he found that bilateral control could be modeled using something called the damped-wave equation, which describes how oscillations, such as waves propagating through a heavy fluid, die out over distance. Once he had a mathematical description of his dynamic system, he used techniques standard in control theory — in particular, the Lyapunov function — to demonstrate that his algorithm could stabilize it.

Horn’s proof accounts for several variables that govern real-life traffic flow, among them drivers’ reaction times, their desired speed, and their eagerness to reach that speed — how rapidly they accelerate when they see gaps opening in front of them. Horn found that the literature on traffic flow instabilities had proposed a range of values for all those variables, and within those ranges, his algorithm works very efficiently. But in fact, for any plausible set of values, the algorithm still works: All that varies is how rapidly it can smooth out disruptions.

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Virtual traffic lights help solve commuting hell

 

Large-scale testing is due to begin next year.

Your average driver spends a week each year stuck in traffic.

So Ozan Tonguz, a telecommunications researcher at Carnegie Mellon University in Pittsburgh, Pennsylvania, is looking to nature for an innovative solution to gridlock. His team is trying to emulate the way in which ants, termites, and bees communicate right of way in busy colonies and hives.

Tonguz’s company, Virtual Traffic Lights, recently patented an algorithm that directs traffic at busy junctions. As cars approach the intersection, they use dedicated short-range communications to quickly exchange information on their number and direction of travel. The largest group of vehicles is given an in-car green light. Cars in the other cluster see a red light and have to wait.

As soon as the biggest group of cars passes through the intersection, the next biggest group is given the green light. Simulations over the past three years have shown the system could reduce commute time for urban workers between 40 to 60 per cent during rush hour.

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SignalGuru uses network of dashboard-mounted smartphones to help drivers avoid red traffic lights

The continuing increase in gasoline prices around the world over the past decade has also seen an increase in the practice of hypermiling – the act of driving using techniques that maximize fuel economy.

One of the most effective hypermiling techniques is maintaining a steady speed while driving instead of constantly stopping and starting. Unfortunately, traffic lights all too often conspire to foil attempts at keeping the vehicle rolling. Researchers at MIT and Princeton have now devised a system that gathers visual data from the cameras of a network of dashboard-mounted smartphones and tells drivers the optimal speed to drive at to avoid waiting at the next set of lights.

The new system, dubbed SignalGuru, was tested in both Cambridge, Massachusetts, and in Singapore. In Cambridge, where traffic signals are on fixed schedules, the researchers say the system was able to predict when lights would change with an average error of only two-thirds of a second and helped drivers cut fuel consumption by an average of 20 percent. In Singapore, where the duration of lights varies continuously according to changes in traffic flow, the error increased to an average of slightly more than one second, with one particularly light in densely populated central Singapore seeing an average error of more than two seconds.

The version of the system used in the tests graphically displayed the optimal speed for avoiding a full stop at the next light, but a commercial version would probably use audio prompts said Emmanouil Koukoumidis, a visiting researcher at MIT who led the project. The researchers also modeled the effect of instructing drivers to accelerate in order to catch lights before they changed, but decided that wasn’t the safest option.

“The good news for the U.S. is that most signals in the U.S. are dummy signals,” (signals with fixed schedules), says Koukoumidis, who launched the SignalGuru project at MIT with Li-Shiuan Peh, an associate professor in the Department of Electrical Engineering and Computer Science who came to MIT from Princeton in fall 2009. But Koukoumidis says even an accuracy of two and half seconds, “could very well help you avoid stopping at an intersection.” He also points out that the predictions for variable signals would improve as more cars were outfitted with the system, collecting more data.

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