greatest promise arguably lies in helping reduce driver distraction. Cell phone distractions have been a factor in high-profile accidents and are generally associated with a large number of automobile accidents.
This work addresses the fundamental problem of distinguishing between a driver and passenger using a mobile ne, which is the critical input to enable numerous safety and interface enhancements. IEEE's detection system leverages the existing car stereo infrastructure, in particular, the speakers and Bluetooth network. IEEE's acoustic approach has the phone send a series of customized high frequency beeps via the car stereo. The beeps are spaced in time across the left, right, and if available, front and rear speakers. After sampling the beeps, they use a sequential change-point detection scheme to time their arrival, and then use a differential approach to estimate the phone’s distance from the car’s center. From these differences a passenger or driver classification can be made. To validate their approach, they experimented with two kinds of phones and in two different cars. IEEE found that their customized beeps were imperceptible to most users, yet still playable and recordable in both cars. IEEE's customized beeps were also robust to background sounds such as music and wind, and they found the signal processing did not require excessive computational resources. In spite of the cars’ heavy multipath environment, IEEE's approach had a classification accuracy of over 90 percent, and around 95 percent with some calibrations. IEEE also found, they have a low false positive rate, on the order of a few percent.
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