Cambridge research shows cooperating driverless cars can speed up traffic by 35%


Researchers from the UK’s University of Cambridge have used automated model cars to show that a fleet of driverless cars working together to keep traffic moving smoothly can improve overall traffic flow by at least 35%.

Many existing tests for multiple autonomous vehicles are done digitally, or with scale models that are either too large or too expensive to perform indoors with fleets of cars.

Starting with inexpensive scale models of commercially-available vehicles with realistic steering systems, the Cambridge University researchers adapted the cars with motion capture sensors and a Raspberry Pi single-board computer, 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.

The team used the small fleet of miniature robotic cars to observe 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. A queue quickly formed behind the stationary 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 static car without having to stop or slow down significantly.

They then tested the fleet in ‘egocentric’ and ‘cooperative’ driving modes, using both normal and aggressive driving behaviors, 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. When the human-controlled car moved around the track in an aggressive manner, the other cars were able to give way to avoid it, improving safety.

The research was conducted by two students, Nicholas Hyldmar, who designed much of the hardware for the experiment, and Michael He, who designed the algorithms, in the lab of Dr Amanda Prorok from the university’s Department of Computer Science and Technology. The study 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. The team now plans 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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Adam joined the company in 1994, and has been News Editor of TTT since 2009. In his other role as Circulation Manager, he helped create the original Traffic Technology International distribution list 23 years ago, and has been working on it ever since. Outside of work, he is a keen fisherman, runs a drumming band, and plays an ancient version of cricket.