CAR DRIVER ASSISTED FOR BLIND SPOT DETECTION SYSTEM
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Using KPI with driversDeskripsi lengkap
Family Driver
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The expert’s team will prepare a clash detection matrix based on the project design and requirements before start the clash detection process.
The expert’s team will prepare a clash detection matrix based on the project design and requirements before start the clash detection process.
Descripción: Clash Detection sp3d
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Crime DetectionFull description
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Drowsy Driver Detection and Warning System for Automobile Safety and Accident Prevention By Kateja Manoj Rameshlal Project Guide Prof. Amit Patwardhan
Agenda
Problem statement
Introduction
Methods used for drowsiness detection
Basic idea- Combination of detection methods
Conclusion
References
Problem statement
Driver Fatique is one of the significant factor in transportation. According to the National Highway Traffic Safety Administration (NHTSA), about 100,000 crashes are caused due to driver drowsiness each year. Effective and reliable driver drowsyness detection system is required.
Introduction Different methods used for Drowsyness detection
Physiological measures -Electroencephalogram measures (EEG) -Non-intrusive ECG measurement -Breath rate and body temperature etc
Lateral position of the vehicle. Use of sensors present in vehicles with navigation system and gyro sensors. Analysis of drivers body motion such as head motionusing accelerometer.
Basic Idea
Virtual reality driving environment simulator like as shown below is used to test the drowsiness detection system. Combination of different detection methods would be applied on driver.
Fig. Layout of driving simulator (for example)[3]
Conclusion
Combination of different detection methods should be used for effective and reliabe drowsy driver detection. Chances of road accidents should be significanty reduced.
References 1. Albert Kircher , Marcus Uddman, Jesper Sandin, “ Vehicle control and drowsiness” [VTIMeddelande 922A. 2002] 2. Nidhi Sharma, V. K. Banga, “Drowsiness Warning System Using Artificial Intelligence” [World Academy of Science, Engineering and Technology 67 2010] 3. Xun Yu, Department of Mechanical and Industrial Engineering University of Minnesota Duluth, “Real-time Nonintrusive Detection of Driver Drowsiness” [ITS Institute]