|University:||Carnegie Mellon University|
|Principal Investigator:||Maxine Eskenazi|
|PI Contact Information:||email@example.com|
|Funding Source(s) and Amounts Provided (by each agency or organization):||$100,000.00|
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|Abstract:||It is well known that drivers who talk on their phones become distracted, putting themselves and others at risk. Despite legislation, people still use their phones while driving, often feeling that they are in control of the situation. Our solution is to monitor the drivers’ speech and other information for evidence that they are distracted and then take appropriate action.|
Using a driving simulator, the Distracted Driving Project has gathered speech and driving data in conditions that vary in their degree of cognitive load. The speech and driving data are time-synced so that coincidences of events in speech and high cognitive load in driving can be modeled. With funding from Yahoo!, we have mined this data to build a model and, from that, a set of algorithms to create a first distraction detector. This detector listens to the driver conversing on the phone and, from information present in the speech signal alone, detects when the driver is becoming distracted.
In order to increase robustness and avoid false alarms, the detector needs to model a larger set of variables, which include information about the driving environment. For this, we will add information from the accelerometer, and other non-speech audio signals. From this, we will create a new model and corresponding set of algorithms. This new robust detector is intended to make everyday use of a distraction detector by the general public much more acceptable. We deliberately do not include any of the car system sensors in our detection algorithm since integration of phone and car systems will often not be available. This is especially the case for younger drivers with older model cars.
The second step in gaining drivers’ trust is to determine what the system should do when it decides that the driver is distracted. A human passenger conversing with a driver can be aware of the difficulty of the driving situation and modify their discussion with the driver appropriately. We will investigate how a phone app can be driving-situation aware and reactive in a similar way. Potential strategies are simply stopping the app, or explicitly indicating that it is going to stop through explicit speech or implicit signaling. We will identify the most effective and graceful methods for a system to disengage from the dialog.
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