Annoyed by yet another traffic jam, the average motorist might claim, “Too many cars, not enough roads.” Traffic experts will say this is too simplistic, as is the call for more public transportation or, in some countries, more bicycle lanes.
Researchers at the University of Cambridge in the U.K., meanwhile, took another approach – robotization and automation to optimize traffic flows. In an experiment with autonomous vehicles that not only could communicate with each other, but also work together to prevent and resolve congestion, they discovered that traffic efficiency increased by 35%, resulting in less congestion.
Cooperative or ego-centric driving
Cooperative driving and rational solution-seeking is key to improve efficiency, something not all human drivers excel at. The importance of this approach was proven in an experiment with 16 autonomous mini-cars, driving on a U-shaped track’s inner and outer lane (see below).
The researchers observed traffic flows in two situations:
- Cars communicated and drove cooperatively (“cooperative”).
- Cars didn’t communicate and didn’t drive cooperatively (“egocentric”).
They 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%.
To increase reality levels, one car was driven remotely by a human motorist, who was instructed to drive aggressively. This showed that a single aggressive driver could increase their efficiency by more than the average, but that all vehicles benefited from communication and cooperation.
Stopped car scenario
In both situations a car stopped on the inner track and sent a signal to all other cars. When the cars were not driving cooperatively, any car behind the stopped car has to stop or to slow down and wait for a gap in the traffic in the other lane. 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 to slow down significantly.
Changing lanes algorithms
A standard 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 improved algorithm developed by the researchers at Cambridge University 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.
A scientific explanation and justification for this experiment is available here.
More self-driving cars required
The solution suggested by the Cambridge University researchers requires a significant penetration of autonomous vehicles. Do consumers want this? Market research shows inconsistent results.
CapGemini Research’s study the Autonomous Car: A Consumer Perspective shows a positive attitude among consumers. They see major advantages, such as reduced fuel use (73% of respondents) and reduced CO2 emissions (71%). More than half are willing to pay 20% (or more) extra for an autonomous vehicle.
Cox Automotive’s study, Evolution of Mobility paints a less rosy picture. According to Cox, consumers are increasingly reluctant to accept autonomous cars. Of those surveyed, 84% want to retain the possibility to drive themselves; only 16% are willing to let technology do all the driving.
Economic, safety and social necessities may force governments across Europe to limit classic auto-mobility, and stimulate or even compel autonomous driving. But for the time being, larger-scale experiments are likely needed for proof of concept.
In the meantime, human drivers would do well to drive more cooperatively – if not for themselves, then for the common good.