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Institute of Integrated Sensor Systems Department of Electrical and Computer Engineering

Institute of Integrated Sensor Systems Department of Electrical and Computer Engineering. Multi-Sensor Soft-Computing System for Driver Drowsiness Detection Li Li, Klaudius Werber, Carlos F. Calvillo, Khac Dong Dinh, Ander Guarde and Andreas König 10-Dec-2012. Introduction

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Institute of Integrated Sensor Systems Department of Electrical and Computer Engineering

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  1. Institute of Integrated Sensor SystemsDepartment of Electrical and Computer Engineering Multi-Sensor Soft-Computing System for Driver Drowsiness Detection Li Li, Klaudius Werber, Carlos F. Calvillo, Khac Dong Dinh, Ander Guarde and Andreas König 10-Dec-2012 • Introduction • Driving Scene Modeling and Hardware Setup • Software Components and Algorithms • Experimental Results • Conclusion and Future Work

  2. Introduction • Major factor in 20 percent of all accidents in the United States in 2006 • The second most frequent cause of serious truck accidents on German highways • Major damage caused by drowsy truck or bus drivers Enhance active safety with advanced driver assistance

  3. Hardware Setup DeCaDrive System • Multi-sensing interfaces • Depth camera • Steering angle sensor • Pulse rate sensor • … … • PC-based soft-computing subsystem • PC-based driving simulator

  4. Hardware Setup • SoA depth camera • Extention of 2D image with distance • Wide field of view • Relatively low computational cost • Robust to lighting variations (active sensing) • Non-intrusive and non-obstructive (eye-safe NIR light source) Microsoft Kinect PMD CamCube SoftKinetic DepthSense

  5. Hardware Setup • Steering angle sensor • Steering behavior of driver • Correlation with driver status and driver intention • Pulse rate sensor • Heart health and fitness • Time domain analysis • Frequency domain analysis embedded

  6. Software Components and Algorithms • Overview of the data processing flow

  7. Feature Computation • Features being computed from multiple sensor measurements

  8. Experimental Results • Test subjects • Five male test subjects • 22 to 25 years old (mean: 23.6, std:1.1) • All have driver‘s license for at least 4 years • No alcohol drinking before test • Experiments • One hour driving simulation for each test subject • 588-minute driving sequence recorded • Ground truth: not drowsy, a little drowsy, deep drowsy • Through self-rated score and response time

  9. Experimental Results • Examples of different sensor features blink frequency low steering percentage mean pulse rate

  10. Experimental Results • Screenshot of online processing of various sensor data Eye pupil and corners Depth image

  11. Experimental Results • Results of ANN based classifier with two training algorithms

  12. Confusion matrix of LM 80 hidden neurons 10-fold cross-validation Confusion matrix of SCG 40 hidden neurons 10-fold cross-validation Experimental Results

  13. Experimental Results • Drowsiness level classification accuracy depending on selected features

  14. Conclusion and Future Work • Contribution • Emerging framework for driver status monitoring and intention detection with multi-sensor soft-computing system • Classification of three different drowsiness levels with up to 98.9% accuracy based on data sets of five test subjects. • Future work • Validation with more statistics and with data from real vehicles • Variance compensation by adaptive learning • Optimization of feature selection with sophisticated heuristics • Utilization of other advanced classification techniques, e.g., SVM • Integration of more embedded sensors with wireless technology

  15. Thank you!

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