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Project

#6 Advanced Driver Distraction Detection System

Principal Investigator
Fernando De La Torre
Status
Completed
Start Date
Jan. 1, 2016
End Date
Dec. 31, 2016
Project Type
Research Advanced
Grant Program
MAP-21 TSET National (2013 - 2018)
Grant Cycle
2016 TSET UTC
Visibility
Public

Abstract

According to the National Center for Statistics and Analysis about 50% of car crashes are due to driver distraction. Monitoring a driver's activities form the basis of a security system that can potentially reduce the number of accidents by detecting anomalous situations. Because distraction while driving is a leading safety issue, the aim of this proposal is to develop an improved driver monitoring system. To achieve this goal, we propose to build a system that concurrently monitors the driver's focus of attention on the road and performs early detection of some of the driver's activities. Our advanced driver detection system (ADDDS) will have one camera facing the driver and another capturing the driver's field of view. 

The proposed system is therefore capable of monitoring both the driver's status and the environment surrounding the car. There are two main modules of ADDDS: (1) Detecting where the driver is looking in the road, and (2) detecting the driver's behavior. To detect where the driver is looking, we propose a method for mapping from the driver's gaze to the exterior view that is based on a 3D reconstruction of the interior and exterior of the car. ADDDS avoids the necessity for re-calibration in a controlled environment cause by changes in the driving position, which is the main drawback of other approaches. To detect the driver's behavior, we will propose a novel temporal classifier for detecting a driver's activities such as talking, texting on the phone, operating the radio, eating, or talking to the passengers. Both systems are combined to create a unique framework that takes advantage of a complete reconstruction of the driver/car environment to reliably estimate driver distraction.
Description

    
Timeline

    

    
Strategic Description / RD&T

    

    
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TRID

    


    
Individuals Involved

    

        
            

                Email
                Name
                Affiliation
                Role
                Position
            

        
        
            
                

                    Ftorre@cs.cmu.edu
                    De La Torre, Fernando
                    Carnegie Mellon University
                    PI
                    Faculty - Research/Systems
                

            
    


    
Budget


    
    
Amount of UTC Funds Awarded
 $83155.00
    
Total Project Budget (from all funding sources)
 $83155.00

    
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