Evaluation of a Driver Alerting System for a Truck Mounted Attenuator

About the project:

The safety of workers and drivers is paramount in a work zone setting. One of the most important tools used in providing that safety is the truck mounted attenuator (TMA). The vehicle is designed to provide guidance and notification to drivers of a lane closure though vehicle mounted signage and lighting. The other aspect of the TMA is that it is designed to absorb the brunt of the energy in the case of a collision with an approaching vehicle. These collisions however remain very dangerous not only for the approaching driver but also the TMA operator. This TMA alert project considers the application of an alarm and additional warning lights that will be triggered based on the potential of a vehicle collision. Using a binocular camera, the system identifies not only approaching vehicles, but also fixed objects typically associated with the work zone such as barrels, cones, and signage. The system then uses an artificial intelligence (AI) approach to identify the work zone layout and fixed objects as well as the potentially intruding vehicle. Following a learning period to train the AI, the AI interface allows for the system to be used in any configuration of work zone without further specific calibration of the roadway areas of interest for an encroaching vehicle.  The purpose of this study is to test the effectiveness of the system though a comparison of the system performance to data collected live in the field.

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