Projects

Driver Status Monitoring In Autonomous Vehicles Using In-Seat Inertial Sensors


Source Organization:
University:Carnegie Mellon University
Principal Investigator:Hae Young Noh, Pei Zhang
PI Contact Information:noh@cmu.edu, peizhang@cmu.edu
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Funding Source(s) and Amounts Provided (by each agency or organization): $100,658.00
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Project Status:Complete
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Abstract:Autonomous vehicles are rapidly defining the automotive industry today. However, the transition from today's manned autos to the future's autonomous vehicles poses many challenges. Unexpected situations (such as snow, unmarked roads, etc.) or manual requests will cause the car to give control back to the human driver. Before the car can hand control back to the driver, the car needs to know the state of the driver, such as attention level, fatigue, and stress. Many single-point on-body sensors and camera systems have been proposed, but these approaches are often limited to controlled laboratory environments or require intrusive sensors on the driver that are difficult to deploy in reality. Additional challenges arise in terms of the impact of vehicle dynamics on sensing noise and constraints on sensor placements. In this project, we will investigate vehicle-based inertial sensors for recognizing driver’s physiological states including posture, movement, muscular activity, and cardiovascular functions. Advantages of the inertial sensor based driver monitoring system come from its simple and non-intrusive nature, which makes deployment easy. A network of small vibrational sensors distributed inside car seats and seat belts will measure vibration induced by the driver’s body. We will then use these vibrations to infer higher level human states, such as stress level, restlessness, and fatigue. The main challenge resides in the high noise level due to the moving vehicle and sensing constraints relying only on contacts. To address these challenges, we plan to utilize multi-sourced, high resolution and high frequency data with multi-resolution signal processing techniques to reject noise and obtain higher level directed information. We will also incorporate physical models of human body and vehicle dynamics in addition to data-driven models. For validation, we are working with our industry partner (Renault) to build our system and embed into cars for laboratory and field testing.
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