In 2007, mathematicians from the University of Exeter showed that the freeway traffic jams that appear to occur for no reason are actually the result of a "backward traveling wave" initiated when a driver slows below a critical speed. This sets off a chain reaction that ultimately results in traffic further down the line coming to a complete standstill. An MIT professor has now developed an algorithm that could be applied to a modified Adaptive Cruise Control (ACC) system to help eliminate such traffic jams.

Last year, Honda announced plans to conduct public-road testing of technology that detects whether a person's driving style is likely to create traffic jams and encourages them to adopt a driving style that would avoid this. At the time, Honda said it would be possible to further improve this system by connecting it to cloud servers that would allow a vehicle's ACC system to automatically sync with the driving patterns of vehicles further up the road.

Berthold Horn, a professor in MIT’s Department of Electrical Engineering and Computer Science, has come up with a somewhat similar approach that would also rely on a vehicle's ACC system, but without the need for the system to connect to the cloud. However, it would require current ACC systems, which only monitor the speed and distance of vehicles in front, to be modified to also take into account the speed and distance of the vehicle traveling directly behind.

Horn describes this system as "bilateral control," as it looks both forward and backward at the same time. By gathering this information, the ACC system is able to keep the car at roughly the midpoint between the vehicle in front and the vehicle behind. In this way, the system is able to avoid slamming on the brakes too hard if the car in front brakes, thereby avoiding causing a large disruption to the car behind that is then amplified as it is passed onto vehicles following behind.

Being a computer scientist, Horn was able to construct a computer simulation to put his algorithm to the test – a sample of which can be seen in the animated GIF below that even includes brake lights and has the bilateral-control algorithm switching on at the one-minute mark.

Image courtesy of Berthold Horn

Although the simulation appeared to verify his hunch, he sought further mathematical proof that came through modeling the bilateral control using the "damped-wave equation," which describes how waves propagating through a heavy fluid die out over distance.

Horn found that his algorithm worked very efficiently when taking into account various values for a range of variables, such as driver reaction times, their desired speeds and how rapidly they accelerate to reach those speeds when a gap opens up in front of them. The only thing that changes as these variables change is the time it takes for the algorithm to smooth out the disruptions.

The major problem facing the implementation of the algorithm is the technology required. Currently, ACC systems are generally only available as an option on high-end vehicles as they rely on relatively expensive sensors such as radar or laser rangefinders. Additionally, these only monitor the speed and distance of the vehicle in front, so Horn's system would require a second system to be installed to monitor vehicles traveling behind the car.

However, Horn does suggest that alternative cheaper technologies, such as digital cameras, could be employed to bring his algorithm to the roads. But these have their own drawbacks.

“There are several techniques,” Horn says. “One is using binocular stereo, where you have two cameras, and that allows you to get distance as well as relative velocity. The disadvantage of that is, well, two cameras, plus alignment. If they ever get out of alignment, you have to recalibrate them.”

Horn's system would also only be effective if a large percentage of vehicles on the road were using it, meaning it probably won't be helping to cut traffic jams anytime soon.

Horn presented his algorithm at the IEEE Conference on Intelligent Transport Systems held earlier this month.

Sources: MIT, University of Exeter