A Survey on Proactive Accident Avoidance System Using IOT

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CSEIT17216 | Received: 05 Feb 2017 | Accepted: 15 Feb 2017 | January-February-2017 [(2)1: 29-34 ]

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A Survey on Proactive Accident Avoidance System Using IOT

Mithilaesh J.

*1

_{, Santhoshkumar K.}

2

_{, Ashwinbalaji P.}

3

_{, Dr. S. Hemalatha}

4

_{, Dr. V. Subedha}

5 1,2,3,4,5

Department of CSE, Panimalar Institute of Technology, Chennai, Tamil Nadu, India

ABSTRACT

Distracted driving is one of the major causes of vehicle collisions. Passively tracking a motive force’s activities constitutes the premise of a car protection machine that may probably reduce the quantity of injuries by using estimating the driver’s attention of interest. Car vehicles are increasingly more being prepared with twist of fate avoidance and caution systems for fending off the potential collision with an external item, which includes every other car or a pedestrian. Upon detecting a potential issue, such structures generally provoke an action to keep away from the collision and/or provide a caution to the automobile operator. As the primary reasons of automobile coincidence have been associated with human factors, they may be classified in one of the two important driving force’s distraction categories (Alcohol consumption, Drowsiness and distracted imaginative and prescient).the principle purpose of this paper is to investigate the diverse present proactive accident avoidance structures and listing out their blessings and disadvantages.

Keywords: PERCLOS, Sagging Posture, Electroencephalographic, Kinect, Adaboost Classifier, Hidden Markov Model, Blood Alcohol Content.

I. INTRODUCTION

Driver drowsiness is a common reason of deadly traffic accidents. The countrywide motorway visitor’s protection administration pronounced that drowsy riding turned into worried in 2.2–2.6% of the full fatal crashes annually throughout the duration 2005–2009. Numerous research have reported that many sleep-associated vehicle injuries arise during the intervals of round 2:00–6:00 A.M. and 14:00–16:00 P.M and it is regularly pointed out that night shifts make drivers mainly inclined. The automation superior driver assistance structures (ADAS) can be labelled into 4 training:

1) Improving belief;

2) Arousing attention to capability dangers; 3) Putting off a caution; and

4) Acting safety manage.

For this reason, ADAS are designed to supplement driver capabilities for notion, cognition, action selection, and movement implementation in dynamic surroundings. One way to lessen the range of

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speed are regularly used to assess driver drowsiness. The Karolinska sleepiness scale is frequently used for subjective self-evaluation. Kitajima et al. proposed a sleepiness score scale primarily based at the facial expressions of the motive force for outside assessment; this scale is often used for outside assessment in Japan. Ohsuga et al modified the Kitajima scale by thinking about the driving force’s resistance to sleepiness. Ishida et al. recommended the use of facial muscular sports corresponding to a drowsy facial expression to estimate motive force drowsiness robotically in actual time. While thinking about the pedal and guidance sports to evaluate driving force drowsiness, steering postpones the lateral fluctuation of the car and popular deviation of the space to a lead car is frequently used. However, maximum of those techniques tend to be unreliable. With physiological measures, making sure accurate judgment is hard due to character differences in physiological residences. Programs in the actual global can also be impractical if they require restraints or invasive motive force-monitoring device. Further, the evaluation of behavioural measures which include eye blinks and driver frame movement is tormented by character differences. It is not clean to calibrate a decision criterion for driving force drowsiness that is applicable to all drivers. Schemes based on drowsy facial expressions may not work successfully or nicely if a driver wears a flu mask or shades. The evaluation of guidance activity for lane-retaining performance is suffering from disturbances consisting of move winds

and road surroundings. Consequently, many

researchers have tried to increase a reliable method for tracking driving force drowsiness based on a combination of direct driving force-related and oblique riding-associated measures.

II. LITERATURE SURVEY

[1] Drowsiness monitoring based on driver and driving data fusion by I. G. Daza, N. Hernandez, L. M. Bergasa, I. Parra, J. J. Yebes, M. Gavilan, R. Quintero, D. F. Llorca and M. A. Sotelo

This paper gives a non-intrusive approach for monitoring motive force drowsiness, based totally on motive force and driving statistics fusion. The proportion of Eye Closure (PERCLOS) is used to estimate the driver’s nation. The PERCLOS is computed on actual time using a stereo vision-primarily based system. The riding facts used is the lateral role,

the steering wheel attitude and the heading mistakes provided by way of the CAN bus. These 3 indicators were studied inside the time and frequency domain. A multilayer perceptron neural community has been educated to fetch a most desirable overall performance rating. This system changed into installed in a naturalistic riding simulator. For evaluation purposes, several experiments had been designed with the aid of psychologists and performed with expert drivers. As ground fact, subjective experts’ manual annotation of the motive force video sequences and driving alerts changed into used. A detection fee of 70% the use of person signs became raised up to 94% with the aggregate of indicators. An explanation about those effects and some conclusion are supplied.

[2] A Survey on Driver Fatigue-Drowsiness Detection System by Indu R. Nair, Nadiya Ebrahimkutty , Priyanka B.R , Sreeja M and Prof. Gopu Darsan

One of the essential reasons for road accidents now an afternoon is due to driving force fatigue. Be it lengthy distant journeying or under the influence of alcohol riding drowsy kingdom leads to volatile crashes which are dangerous to lives as well. To triumph over such accidents a few methods have to be developed this is viable to all car drivers. This paper is a survey primarily based on various techniques for preventing road injuries and designs on drowsiness detection methods which were proposed and feature advantages and disadvantages.

[3] Multi-Sensor Soft-Computing System for Driver Drowsiness Detection by Li, Klaudius Werber, Carlos F. Calvillo, Khac Dong Dinh, Ander Guarde and Andreas Konig

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non-intrusive manner. Multi-sensor records fusion on function degree and multilayer neural network facilitate the class of driving force drowsiness level primarily based on which a caution may be issued to save you site visitors accidents. The supplied approach is carried out on an integrated smooth-computing system for riding simulation (DeCaDrive) with multi-sensing interfaces. The classification accuracy of 98.9% for up to three drowsiness ranges has been completed based totally on statistics sets of 5 test subjects with 588-minute riding sequence.

[4] Multi-Source Information Fusion for Drowsy Driving Detection Based on Wireless Sensor Networks by Liang Wei, Razali Jidin, S. C. Mukhopadhyay and Chia-Pang Chen

Drowsy using is a primary motive of avenue injuries. This paper analyses the drivers’ conduct within the nation of fatigue riding and introduces the modern-day tendencies of drowsy driving detection era. in this look at we also advocate a drowsy driving detection based totally on the driving force’s physiological alerts inclusive of eye activity measures, the inclination of the driving force’s head, sagging posture, coronary heart

beat price, skin electric capability, and

electroencephalographic (EEG) sports, in addition to reaction characteristics, decline in gripping force on the steering wheel and lane preserving characteristics. With the aid of developing a hierarchical version this is capable of gather the sensing records, analyse the driving conduct and the reactions to the driving force, it can offer a secure and a secure driving environment. Combining unique indications of drowsiness and processing the contextual statistics to expect whether a driver is drowsy, the gadget not simplest problems a caution for the driving force, but also provide the drowsy riding records to transportation manage middle and other cars if important.

[5] Driver distraction detection and recognition using RGB-D sensor by Céline Craye and Fakhri Karray

Motive force inattention evaluation has turn out to be a very active subject in shrewd transportation systems. Primarily based on lively sensor Kinect and pc imaginative and prescient gear, they have constructed an efficient module for detecting driving force distraction and recognizing the type of distraction. Based totally on colour and intensity map statistics

from the Kinect, their gadget consists of 4 sub-modules. They name them eye behaviour (detecting gaze and blinking), arm role (is the right arm up, down, proper of ahead), head orientation, and facial expressions. Every module produces relevant records for assessing motive force inattention. They may be merged collectively later on the usage of two exceptional type techniques: AdaBoost classifier and Hidden Markov model. Evaluation is carried out using a driving simulator and eight drivers of different gender, age and nationality for a complete of more than 8 hours of recording. Qualitative and quantitative results show strong and accurate detection and recognition capability (85% accuracy for the sort of distraction and 90% for distraction detection). Furthermore, every module is acquired independently and can be used for other styles of inference, which includes fatigue detection, and will be implemented for actual automobiles structures.

[6] Hybrid Driver Fatigue Detection System Based on Data Fusion with Wearable Sensor Devices by QuanZhe Li, Juan Wu, Shin-Dug Kim and Cheong-Ghil Kim

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[7] Driver -Vehicle-Interface (D-V-I): A Method for Averting Driver’s Drowsiness by Priyanaka Sawant, M.Z.Shaikh

A Drowsy motive force Detection system has been evolved, the use of a non-intrusive device vision based principles. The gadget makes use of a small monochrome safety digital camera that factors directly towards the driving force’s face and video display units the driving force’s eyes which will stumble on fatigue. In one of these cases whilst fatigue is detected, a warning sign is issued to alert the motive force. This record describes the way to find the eyes, and also a way to determine if the eyes are open or closed. The system deals with using records acquired for the binary model of the photograph to discover the edges of the face, which narrows the region of where the eyes may additionally exist. As soon as the face region is found, the eyes are discovered through computing the horizontal averages within the area. Contemplating the knowledge that eye regions inside the face gift fantastic intensity adjustments, the eyes are positioned by means of finding the tremendous depth adjustments within the face. Once the eyes are located, measuring the distances among the intensity adjustments in the eye place decide whether or not the eyes are open or closed. A massive distance corresponds to eye closure. If the eyes are located closed for targeted consecutive frames, the machine attracts the realization that the motive force is falling asleep and issues a caution sign. The device is likewise able to stumble on whilst the eyes can't be observed, and works below affordable lighting conditions.

[8] Alcohol Detection and Vehicle Controlling by Pratiksha Bhuta, Karan Desai, Archita Keni

This device is aimed toward making car riding safer than earlier than. That is implemented the usage of Arduino. they have got derived the driving force’s circumstance in real time environment and that they recommend the detection of alcohol using alcohol detector related to Arduino such that after the extent of alcohol crosses a permissible limit, the car ignition device will turn off and the GPS module will capture the prevailing location of the vehicle. Additionally the GSM module will routinely send distress message to police or own family contributors.

[9] Drunken Drive Protection System by J.Vijay, B.Saritha, B.Priyadharshini, S.Deepeka, R.Laxmi

These days nearly maximum of the countries are forcing the motor riders to put on the helmet and not to use the vehicles whilst the individual is in drunken situation. But nevertheless in lots of places, the policies are being violated by using the customers. So as to triumph over this trouble, a clever gadget has been embedded in the helmet itself. The sign detected by means of IR sensor from the earlobe region and an alcohol sensor might be transmitted to the car control circuit. It will no longer activate the car, while the consumer is without helmet or in drunken condition. The system containing the GPS receiver will provide the geometric coordinates to the manage unit. Based totally on this coordinates the person cannot force the car into no entry or no parking areas. If he enters into the confined vicinity, buzzer will get activated and vehicle velocity additionally receives controlled. in addition to the above, while an twist of fate happens the machine will begin alarm and if the consumer tries to suppress the caution alarm then SMS will now not be despatched else it will likely be sent to the consumer’s spouse and children/pals. This touch facts coded inside the device also can be modified as consistent with the users need. All through the theft, the cutting-edge location of the automobile can be recognized by way of sending the message from the person to the smart incorporated machine. By means of this way the recovery of the vehicle is likewise possible through GPS-GSM verbal exchange.

[10] Automatic Drunken Drive Prevention System by M. Kousikan, M. Sundaraj

Riding even as both intoxicated or inebriated is dangerous and drivers with excessive blood alcohol content (BAC) are at elevated danger of car injuries, motorway injuries and vehicular deaths. Prevention measures evaluated consist of license suspension or revocation, impounding or confiscating vehicle plates, enforcing open field bans, increasing high-quality

consequences, jail, mandating schooling for

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which is included with the steering wheel. Ethanol has more potential to soak up IR rays. So, they use an IR Sensor that's mounted on steerage. An IR source led-894 directs IR power thru the sensor constantly. If the float of IR rays is interrupted by absorption of alcohol vapour a relay circuit is activated. This relay circuit has managed over the fuel supply device and it cuts-off the fuel supply to the engine. This makes the car to return to halt slowly .The higher the concentration of ethanol, the more infrared absorption takes place (same way that a Cooler absorbs visible mild, alcohol absorbs infrared mild).

[11] Smart Drunken Driver Detection and Speed Monitoring System for Vehicles by Bandi Sree Geeta, Diwakar R. Marur

The paper presents a brand new approach in the direction of vehicle protection and security to decrease the wide variety of injuries brought about because of the drunken drivers. It has a clever digital system which continuously video display units the alcohol content within the air surrounding by using the body of the protagonist. Pace of the automobile varies at the content material of alcohol detected. Automobile based totally countermeasure system gradually video display units the speed locking gadget of the automobile walking from excessive (100kmph), medium (60-80kmph) and low (40kmph) which facilitates the driving force to attain destination thoroughly. In excessive situations the gadget disables the automobile with the aid of switching off the ignition. The global Positioning device (GPS) captures the area and sends statistics to the authorities with the assist of a global device for cell (GSM) tool with the onetime password. Best after the password is entered the vehicle can be restarted. within the again cease, all of the driving situations, quantity of alcohol detected, vehicle pace and the location is uploaded into internet server automatically and correctly as quickly because the signal of alcohol detected for investigation cause. All the capabilities within the mission are achieved with the help of ARM 7 based totally LPC 2148 microcontroller.

[12] Unsafe Driving Detection System using

Smartphone as Sensor Platform by Prarna Dhar , Sarika Shinde, Nikhil, Jadav , Anirudha Bhaduri

Dangerous driving especially consists of riding either rashly or driving underneath the have an impact on (DUI) of alcohol, is a major reason of traffic injuries at some stage in the world. On this paper, they recommend a highly green system which facilitates at early detection and alert of risky vehicle maneuvers commonly related to rash riding. The entire gadget requires simplest a cell phone in an effort to be placed in vehicle and with its in-built accelerometer and orientation sensor. After installing software at the mobile telephone, it's going to compute accelerations based on sensor readings and evaluate them with regular unsafe driving patterns extracted from actual driving tests.

[13] Integrated Drunken Driving Prevention System by T. Shyam Ramanath, A. Sudharsan, and A. Kavitha

As is unnecessary to mention; a majority of injuries, which arise, are because of under the influence of alcohol using. As such, there may be no effective mechanism to prevent this. Here they have designed an integrated gadget for the identical reason. Alcohol content material inside the motive force’s frame is detected by using an infrared breath analyser positioned on the guidance wheel. An infrared mobile directs infrared power via the sample and any unabsorbed energy at the other aspect is detected. The higher the awareness of ethanol, the extra infrared absorption takes place (in plenty the identical way that a sunglass lens absorbs visible light, alcohol absorbs infrared mild). Thus the alcohol stage of the driving force is continuously monitored and calibrated on a scale. While it exceeds a particular restriction the gasoline supply is cut-off. If the device is eliminated also, the fuel supply may be routinely cut off or an alarm is sounded depending upon the requirement. This doesn't occur abruptly and special signs are constant on the again to keep away from inconvenience to different drivers using the motorway alerts. Body work for integration of sensors and control module in a scalable multi-agent machine is furnished .A SMS which contains the contemporary GPS location of the vehicle is sent thru a GSM module to the police manipulate room to alert the police. The gadget is fool proof and the motive force cannot tamper with it easily. Thus it gives an effective and cost powerful answer for the problem of under the influence of alcohol driving in motors.

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The proactive system judges the motive force’s country in a multi-layered manner thru the interaction between the driving force and the gadget further to executing the first- and 2nd-stage controls to keep safety. This paper has discussed various proactive structures which are effective for preventing of sleep-associated vehicle accidents with the aid of listing out their benefits and disadvantages.

IV. REFERENCES

[1]. I. G. Daza et al., "Drowsiness monitoring based on driver and driving data fusion," 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, 2011, pp. 1199-1204.

[2]. Indu R. Nair ,Nadiya Ebrahimkutty , Priyanka B.R , Sreeja M, Prof. Gopu Darsan, A Survey on Driver Fatigue-Drowsiness Detection System,” Journal Of Engineering And Computer Science (IJECS), https://www.ijecs.in, Volume 5 Issue

11, Nov 2016, 19237-19240, DOI:

10.18535/ijecs/v5i11.92.

[3]. Multi-Sensor Soft-Computing System for Driver Drowsiness Detection by Li, Klaudius Werber, Carlos F. Calvillo, Khac Dong Dinh, Ander Guarde and Andreas Konig in 2012 online conference on soft computing in industrial application, December.

[4]. L. Wei, S. C. Mukhopadhyay, R. Jidin and C. P. Chen, "Multi-source information fusion for drowsy driving detection based on wireless sensor networks," 2013 Seventh International Conference on Sensing Technology (ICST), Wellington, 2013, pp. 850-857.

[5]. Driver distraction detection and recognition using RGB-D sensor by Céline Craye and Fakhri Karray.

[6]. Hybrid Driver Fatigue Detection System Based on Data Fusion with Wearable Sensor Devices by QuanZhe Li, Juan Wu, Shin-Dug Kim and Cheong-Ghil Kim.

[7]. Driver -Vehicle-Interface (D-V-I): A Method for Averting Driver’s Drowsiness by Priyanaka Sawant, M.Z.Shaikh in International Journal of Recent Advances in Engineering & Technology (IJRAET), Volume-3, and Issue 1, 2015.

[8]. Alcohol Detection and Vehicle Controlling by Pratiksha Bhuta, Karan Desai, Archita Keni,

International Journal of Engineering Trends and Applications (IJETA) – Volume 2 Issue 2, Mar-Apr 2015.

[9]. Drunken Drive Protection System by J.Vijay,

B.Saritha, B.Priyadharshini, S.Deepeka,

R.Laxmi, and International Journal of Scientific & Engineering Research Volume 2, Issue 12, and December-2011.

[10]. Automatic Drunken Drive Prevention System by M. Kousikan, M. Sundaraj, International Journal of Students Research in Technology & Management Vol 2 (02), March-April 2014. [11]. Smart Drunken Driver Detection and Speed

Monitoring System for Vehicles by Bandi Sree Geeta, Diwakar R. Marur, International Journal of Advanced Technology in Engineering and Science, Volume No.03, Issue No. 03, March 2015.

[12]. Unsafe Driving Detection System using

Smartphone as Sensor Platform by Prarna Dhar , Sarika Shinde, Nikhil, Jadav , Anirudha Bhaduri, International Journal of Enhanced Research in Management & Computer Applications, Vol. 3 Issue 3, March-2014.

[13]. Integrated Drunken Driving Prevention System by T. Shyam Ramanath, A. Sudharsan, and A.

Kavitha, World Academy of Science,