From our blog at Decision Points : Tuesday, 21 Feb 2012

From: Science News >> Technology

In 2008, according to the National Highway Traffic Safety Administration, 2.3 million automobile crashes occurred at intersections across the United States, resulting in some 7,000 deaths. More than 700 of those fatalities were due to drivers running red lights. But, according to the Insurance Institute for Highway Safety, half of the people killed in such accidents are not the drivers who ran the light, but other drivers, passengers and pedestrians.

In order to reduce the number of accidents at intersections, researchers at MIT have devised an algorithm that predicts when an oncoming car is likely to run a red light.

Based on parameters such as the vehicle’s deceleration and its distance from a light, the group was able to determine which cars were potential “violators” — those likely to cross into an intersection after a light has turned red — and which were “compliant.”

The researchers tested the algorithm on data collected from an intersection in Virginia, finding that it accurately identified potential violators within a couple of seconds of reaching a red light — enough time, according to the researchers, for other drivers at an intersection to be able to react to the threat if alerted. Compared to other efforts to model driving behavior, the MIT algorithm generated fewer false alarms, an important advantage for systems providing guidance to human drivers. The researchers report their findings in a paper that will appear in the journal IEEE Transactions on Intelligent Transportation Systems.

Researchers analyzed data from 15,000 vehicles approaching a heavily instrumented intersection, and found their algorithm was accurate 85% of the time. They hope to create a system that can monitor approaching traffic and spot red-light runners in enough time to give a warning to other motorists to help avoid collisions.