In July, at the Association for Computing Machinery’s MobiSys conference, researchers from MIT and Princeton University took the best-paper award for a system that uses a network of smartphones mounted on car dashboards to collect information about traffic signals and tell drivers when slowing down could help them avoid waiting at lights. By reducing the need to idle and accelerate from a standstill, the system saves gas: In tests conducted in Cambridge, Mass., it helped drivers cut fuel consumption by 20 percent. Cars are responsible for 28 percent of the energy consumption and 32 percent of the carbon dioxide emissions in the United States, says Emmanouil Koukoumidis, a visiting researcher at MIT who led the project. “If you can save even a small percentage of that, then you can have a large effect on the energy that the U.S. consumes,” Koukoumidis says.
Of course speeding up to beat the light and thus avoid excessive car idling sounds great to the frustrated driver. However, there are safety concerns too.
The system is intended to capitalize on a growing trend, in which drivers install brackets on their dashboards so that they can use their smartphone as a GPS navigator while driving. But unlike previous in-car cellphone applications, the new system, dubbed SignalGuru, relies on images captured by the phones’ cameras. According to Koukoumidis, the computing infrastructure that underlies the system could be adapted to a wide range of applications: The camera could, for instance, capture information about prices at different gas stations, about the locations and rates of progress of city buses, or about the availability of parking spaces in urban areas, all of which could be useful to commuters.
The researchers did model the effect of instructing drivers to accelerate in order to catch lights before they changed, but “we think that this application is not a safe thing to have,” Koukoumidis says. The version of the application that the researchers used in their tests graphically displays the optimal speed for avoiding a full stop at the next light, but a commercial version, Koukoumidis says, would probably use audio prompts instead.
Koukoumidis envisions that the system could also be used in conjunction with existing routing software. Rather than recommending, for instance, that a car slow to a crawl to avoid a red light, it might suggest ducking down a side street.
This would truly be a smart computer but it needs a lot of current as well as historical data to make these last second decisions.
Article by Andy Soos, appearing courtesy Environmental News Network.
