- December 1, 2016
- Posted by: Jeff Kish
- Categories: AI & Advanced Computing News, AI Transportation Systems News, Data Fusion News, Healthcare Research Technologies News, Latest News, Modeling, Simulation & Visualization News, Research & Development News, Wearable Design & Manufacturing News
An accurate, unobtrusive, and automatic system to detect driver distraction and fatigue would help reduce the number of commercial motor vehicle crashes. Existing detection technologies are not always reliable and accurate in the operating environment, and cannot handle individual variations in drivers. To address this, IAI and collaborators at RPI, Virginia Tech, and Meritor Wabco will continue to develop an innovative Multi-Modal Driver Distraction and Fatigue Detection/Warning System (MDF). MDF has four major modules. The first module makes measurements based on the driver’s behavior. This includes measuring driver pose and psychophysiological measures of alertness such as percentage of eyelids closure and average eye closure speed, and detecting yawning and gestures. The Driving Style Module detects erratic inter/intra-lane driving and erratic speed variations based on yawn rate sensor and/or controller area network bus signals. The Physiological sensing module uses wearable devices to measure parameters like heart rate and monitor sleep and activity. The Sensor fusion, Recording, Warning and Individualization module manages inputs from other modules, takes appropriate action and issues warnings when necessary. The innovative and practical MDF system detects driver distraction and fatigue quickly and reliably under a wide variety of operating conditions. By fusing different modalities of sensor information, MDF can provide unique and customized feedback for different drivers with varied physiological features. MDF can be applied commercially in regular passenger vehicles, and can detect dangerous habits like texting while driving. It can also be used to detect distraction/fatigue in passenger and fighter airplane pilots, and to improve the state of the art in any operator fatigue and distraction detection system.