Real time detection system of driver distraction.pdf
- 1. Real-Time Detection System of Driver Distraction Using Machine Learning
- 2. Contents… Machine Learning Introduction Driver Distraction Driver Distraction Mitigation Detection Algorithm Methods Advantages / Disadvantages Applications
- 3. MACHINE LEARNING Machine learning is a scientific discipline concerned with the design and development of algorithms that allow machines to mimic human intelligence.
- 4. Introduction 80% of crashes and 65% of near crashes involved some sort of driver distraction. Teens are 4x more likely to be in a wreck than drivers over age 30. Motor vehicle crashes are the leading cause of death for 16-20 year olds.
- 5. Driver Distraction • Driver distraction and inattention has become a leading cause of motor-vehicle crashes IVIS • Driver distraction represent a big challenge for developing IVISs
- 6. Types of Distractions: There are 3 types of distractions: Visual Distractions: Anything that takes your eyes off the road. Manual Distractions: Anything that takes your hands off the steering wheel. Cognitive Distractions: Anything that takes your mind off driving.
- 7. Distractions: All distractions can be dangerous and life threatening but texting is one of the most dangerous distractions because it involves all three types of distractions. Other distractive activities include: » Using a cell phone » Eating and drinking » Talking to passengers » Grooming » Reading, including map » Using PDA or navigation system » Watching a video » Changing the radio station, CD, Mp3 player or other device
- 8. How Cell Phones Distract Visual – Eyes off road Mechanical – Hands off wheel Cognitive – Mind off driving CHALLENGE: Drivers don’t understand or realize that talking on a cell phone distracts the brain and takes focus away from the primary task of driving.
- 10. Sleepy Driving… 100,000 reported crashes per year are as a result of drowsiness. 1,500 of them result in deaths. 55% of those crashes were caused by drivers under the age of 25.
- 11. Driver distraction mitigation systems Distraction detection is a crucial function oCognitive distraction oVisual/manual distraction oSimultaneous(dual) distraction
- 12. Driver state----------------- · Physiological responses · eye glances · fixations, saccades, and smooth pursuits ... Driver input----------------- · Steer · Throttle · Brake ... Vehicle state--------------- · Lane position · Acceleration · Speed ... Visual/Manual distraction Cognitive distraction Model-based Driver Distraction Detection Mitigation strategy Focus of dissertation Sensor Techology Mitigation System Strategy n Strategy 2 Strategy 1 . . . An overview of driver distraction mitigation systems
- 13. Detection algorithm for driver distraction • Driving is complex and continuous human behavior • Machine learning approaches are suitable to detect driver distraction o Linear regression, decision tree, Support Vector Machines (SVMs), and Bayesian Networks (BNs) have been used to identify various distractions Support Vector Machines (SVMs) Bayesian Networks (BNs)
- 14. Bayesian Networks (BNs) To model probabilistic relationship among variables ◦ wide applications, especially modeling human behavior Three kinds of variables ◦ Hypothesis, evidence, hidden H E3E2E1 S Bayesian Networks (BNs) Cognitive distraction Eye movements Driving performance Eye movement pattern
- 15. Methods SURT(surrogate visual research task) display on the right part of the driving simulator cockpit
- 16. Advantages… Intelligent Decisions Self modifying Multiple iterations Method implementation Use of different methods Test in diverse conditions. Disdvantages…
- 17. Applications Computer vision : design and implementation of algorithms that can automatically process visual data. Information retrieval : Technologies in order to help solve complex and challenging business problems. Robot locomotion : Capabilities for robots to decide how, when, and where to move
- 18. REMEMBER… The life you save could be your own!
- 19. Conclusion.. The most ambitious goals of automatic learning systems is to mimic the learning capability of humans, and the capability of humans to drive is widely based on experience, particularly on the possibility of learning from experience. ML approaches can outperform the traditional analytical methods. Moreover, a human’s mental and physical driving behavior is non deterministic.
- 20. QUESTIONS...
- 21. THANKYOU