Real Time Labeling of Driver Behavior in Real World Environments
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Seebeck, Christopher
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Abstract
Vehicle systems that use driver behavior data to determine safe and unsafe behavior need to operate in real time and in real chaotic environments. The research in developing these systems do not have publicly accessible data sets that would aid in research and development. In order to create these data sets, experiments and data collections need to be performed in an unconstrained real world environments or in a highly constrained environment. This thesis proposes a tool to collect real time data in unconstrained real world environment, called Live Driving Detection (LiDD). LiDD labels driver head rotations to determing where the driver is looking and combines the state of the car from the CANBus network to add more context to the produced data. The labeling process is designed to be simple in order to quickly label a given instance of data called a frame. LiDD is able to output labeled driver data fused with vehicle state at approximately 6 HZ. LiDD's utility was evaluated in three common real world environments: a suburban, a major highway, and a city environment. This research shows that LiDD and it's resulting data sets can be developed without requiring expensive equipment and that it's data will be useful for future research and development of Advanced Driver-Assistance Systems (ADAS).
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Face detection, Data collection, CANBus, Head position, Driver behavior, Real time data labeling