institute of integrated sensor systems department of electrical and computer engineering
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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 - PowerPoint PPT PresentationTRANSCRIPT
1 Li Li [WSC17]
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 Driving Scene Modeling and Hardware Setup Software Components and Algorithms Experimental Results Conclusion and Future Work
2 Li Li [WSC17]
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
Introduction
Enhance active safety with advanced driver assistance
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Hardware Setup
DeCaDrive SystemMulti-sensing interfaces
• Depth camera
• Steering angle sensor
• Pulse rate sensor
• … …
PC-based soft-computing subsystemPC-based driving simulator
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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)
Hardware Setup
PMD CamCubeMicrosoft Kinect SoftKinetic DepthSense
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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
Hardware Setup
embedded
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Software Components and Algorithms
Overview of the data processing flow
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Feature Computation
Features being computed from multiple sensor measurements
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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
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Experimental Results
Examples of different sensor features
blink frequency
low steering percentage
mean pulse rate
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Experimental Results
Depth image Eye pupil and corners
Screenshot of online processing of various sensor data
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Results of ANN based classifier with two training algorithms
Experimental Results
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Confusion matrix of LM• 80 hidden neurons
• 10-fold cross-validation
Confusion matrix of SCG• 40 hidden neurons
• 10-fold cross-validation
Experimental Results
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Drowsiness level classification accuracy depending on selected features
Experimental Results
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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 Li Li [WSC17]
Thank you!Thank you!