Non-intrusive Driver Fatigue and Stress Monitoring Using Ambient Vibration Sensing
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2017-01-01
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Abstract:Autonomous vehicles will define the automotive industry in the near future. Autonomous vehicles are expected to improve the space utilization of the road systems by eliminating inefficiencies due to human driving (e.g., large distances between cars to allow for slow human reaction, parking needs after commute, accidents due to driver distraction, etc.), while providing extra free time to the drivers [1-3]. The current state of the art involves the use of externally and internally mounted sensors, such as laser rangefinders, cameras, inertial sensors, infrared sensors, etc., to provide the autonomous car with rich view of the world around it. With these sensors the car can drive autonomously under fairly regular and perfect conditions. However, the autonomous car would have to give back control to a capable driver when it is confronted by unusual road or weather conditions (e.g., snow covered roads with invisible road lane markings, other aggressive road users, unexpected events such as road lane closures, etc.). Such conditions may interfere with, or even blind, the embedded on-board sensors. Whereas human drivers have the ability to compensate and adapt to such conditions, the autonomous vehicle would be limited to only what its sensors can perceive. Before giving control back to the driver, it is essential for the car to know/estimate the state of the driver and determine whether the driver is capable of taking control or the car needs to take other cautious actions. For example, handing back control to a driver who was sleeping, startled and overly stressed about the situation, or even an absentee driver that was away from the driver's seat, moving around in the cabin of the car, would be dangerous. By monitoring the state of the driver through her/his movements and other physiological variables, we can avoid such situations. Prior work has explored a number of sensors to maintain attention level of the driver [4-7]. These works often have sensing requirements that require direct contact with the driver, making them unsuitable for casual drivers. Another approach utilizes camera-based systems that monitor the driver [5, 7]. These systems are often sensitive to different lighting and line-of-sight limitations. Furthermore, these works focus on maintaining the driver’s attention, as oppose to understand the level of inattention, stress and physical fatigue due to the current driver state. To this end, we develop data analysis methods to 1) extract detailed driver’s physiological states (including movement, cardiovascular functions) and 2) infer higher level states (including stress, physical fatigue, and their physiological indicators such as heart rate and breathing rate), under various driving scenarios. The main challenge resides in high noise level due to the moving vehicle and sensing constraints relying only on contacts. To address these challenges, we utilize signal processing for multi-sourced, high-resolution and high frequency data with hybrid modeling approach to minimize uncertainties and obtain reliable information.
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