International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
Driver Distraction Detection System Using Intelligent Approach Of Vision Based System. Mayuresh Adhikari*1, Pradnyesh Bhalange #2 Anand Bhanvase#3, Chaitanya Bharambe*4 ,Prof. Shweta Koparde*5
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Abstract - Analysis of a driver’s head behaviour is a integral
such as music players, PDA devices, mobile phones, etc.—are also siphoning off a growing part of the driver’s attention, increasing the number of traffic incidences and even accidents. The study indicates that wireless devices, passenger related distraction (mostly conversation), and invehicle distraction sources are the most frequent reasons for incidences. Consequently, the automotive industry has paid more interest in controlling in-vehicle human– machine interface (HMI), including third-party products, in order to make driving more comfortable and more importantly to accentuate traffic safety. Traffic accidents causes researches by Indiana University have indicate that there are 85% accidents caused by drivers and 10% accidents caused by vehicles while only 5% by driving environment. Unsafe driving behaviours cause various accidents, such as driving over the speed limit, illegal overtaking, fatigue driving, drinking driving and so on. As far as freeway traffic accident statistics, accidents caused by driver's distraction accounted for 6% to 8% of the total number of accidents and accounted for 22% -24% of the number of fatal accidents. Driver’s distraction has become the biggest threat to the security in the vehicle.
part of a driver observation system. In our system the head position and eye position are strong indicators of a driver’s focus of attention. We have studied semi-supervised strategies for driver distraction detection in real driving conditions to reduce the rate of accidents. Laplacian support vector machine and semi-supervised extreme learning machine were evaluated mistreatment eye and head movements to classify 2 driver states: attentive and cognitively distracted. The planned system tracks facial expression and analyses their geometric configuration to estimate the pinnacle cause employing a 3-D model. In our system when the driver get distracted from driving for some amount of time the machine will detect and then a small alarm/message will help the driver to get back his attention on his driving Key Words: — Raspberry-pi 3, camera module, ANN, Risky visual scanning pattern, beep alarm
1.INTRODUCTION ( Size 11 , cambria font)
The “distracted driving” is a perennial problem that draws attention from the public, policymakers and researchers. The definition for diver distraction is: “the diversion of attention away from activities critical for safe driving towards a competing activity.” A vast variety of activities performed inside the vehicle can become potential distraction, including operating In-vehicle Information Systems (IVISs)[4]., such as navigation and entertainment systems. The research on
Artificial intelligence is intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal. To enhance the accuracy and ability of the modern devices, we need apply the intelligent techniques to make them smarter. Modern day devices are efficient enough to give the required output but are not smarter to take actions on their own. So we took a step to design a device that could detect the driver's detection using intelligent algorithms. According to the environment in the car it would give the required output. kind of pagination anywhere in the paper. Do not number text heads-the template will do that for you.
driver distraction started back in early 1990s, e.g., distractions caused by cell phones were found to significantly affect drivers’ capability of responding to critical situations. In the United States, using cell phones during driving alone causes thousand. Driver fatigue and distraction detection needs real time, accuracy and no-load for driver. But the existing research cannot meet the demand completely. Aimed to overcome above problems.
Finally, complete content and organizational editing before formatting. Please take note of the following items when proofreading spelling and grammar:
3. EXISTING ALGORITHMS Eyes off forward roadway (Klauer et al., 2006)[1.]. As described in Chapter 3, this algorithm defines visual distraction as a cumulative glance away from the road of 2 seconds within a 6-second running window. Because the original algorithm defined the 6second window assuming an identifiable action (i.e., lead vehicle braking) occurred during
2. PROJECT NEED Modern vehicles are full of driver-assistive electronics (multimedia displays, navigator, climate control, parking radar, etc.). In addition, third-party entertainment facilities—
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