Prevent safety has been the main concern for

Prevent Vehicle Collision by Different Methods Nikhil Joseph Saji                                                                      Basil C Sunny                Department Of Computer Science                                                     Department Of Computer Science                                               Adi Shankara Institute Of Engineering and Technology             Adi Shankara Institute Of Engineering and Technology                                 Kalady 683574                                                                                Kalady 683574                          [email protected]                                                               [email protected]

ac.in               Abstract The topic is related to the vehicle collision avoidance system.All the other papers of vehicle collisionavoidance system like ship, cycle etc.

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uses sensors that only avoid collisions and was only effectivewithin certainspeed ranges. In this paper, a scooter collision avoidance system is proposedthat canavoid accidents at intersections and also warns the other scootervehicles. The relevance of thispaper is that here infrastructure basedsolutions such as those utilizing radar or camera are notconsidered. Itadvances by using a smartphone that provides high penetration rate. The paperhasmany advantages like low cost, not affected by obstructions, functions atevery intersections. Keywords: Machine learning, collision avoidance, red-lightrunner, scooter, motorcycle. I.

    INTRODUCTION s cooter is a typeof motorcycle which is the most important transportation means within severalcountries. The fuel efficiency of scooters is at least two times better thanthat of cars and its sale price is only about one-tenth of that of regularpassenger cars .Due to their smaller sizes, it is much easier for scooters topass through traffic congestion, hence shortens the time to reach the destination.This helps the people who live in urban areas and suburban areas to prefer theuse of scooters over cars.   But alsothere are problems caused by the high density of scooters. In particular, roadtraf?c safety has been the main concern for the scooter riders.

Statistics showthat, most of deaths in traf?c accidents are caused by those involvingscooters. It also showed that the accidents were much less for one travellingin the cars than the scooters. This is because when the scooter is used one needsto maintain his/her balance while riding but the car provides a shelter withininside. Many advanced safety features has also resulted in the favor ofpassenger cars. Even though the safety measures can be used for scooters theproblem is the cost for sensors, such as cameras and radar sensors, and costlyprocessing circuits. Examples include Blind Spot Information System (BLIS),Line Departure Warning System (LDWS), pedestrian detection system, and forwardcollision warning system.

When the problem was considered as a serious issuethey came to the conclusion that the red light runners are the cause for thescooter collision.          Scooter collision avoidance systemcan identify red-light runners (RLRs) at intersections .The system would advisethe RLR to slow down immediately and warn nearby vehicles on the intersectingroad in real time. This scooter collision technique is done using smart phonescarried by scooter riders. RLR classifier is used for learning and predictingRLR behavior II.    mETHODOLODY 1.Red EyeArchitecture                  Fig 1 : Red Eye Architecture 2.Data collectorThedata collector periodically obtains data from the GPS, the accelerometer, andthe camera in the smartphone.

Red Eye utilizes the data from GPS to generate anumber of features such as·        Distance to theIntersection ·        Acceleration·        Traffic lightstatus   3.RLRclassifier                                                       Fig 2: RLR Classifier The RLR classifier predictwhether a RLR behavior is likely to happen. 5.

Distance-based SVM (D-SVM) D-SVM takes one set of sensordata every 5 meters. The algorithm starts collecting the data when the scooteris X meters from the intersection and stops when the scooter is Y meters fromthe intersection to perform a prediction. The SVM prediction model returns aconfidence value and compare it with Pre-configured threshold. 6.Multi-Distance-based SVM(MD-SVM ) MD-SVM is designed to addressthe problem of D-SVM.If confidence value of that prediction is higher than thePre-configured threshold,it reports RLR behavior .The system waits for thescooter to travel for another 5 meters to perform the next prediction .

Thethreshold for the confidence value were initially set to a higher value, andthen gradually decreased by multiplying the original threshold with a R value(0 oR1). MD-SVM allows the system to report a RLR event early, when there isstrong evidence in the data.    4.Warning Message Transmitter/Receiver Fig3: Warning Message Transmitter/Receiver   Almost all modern smartphonesare equipped with an IEEE 802.11b/g/n WiFi radio. Red Eye incorporated acustom-made range extender.

Antenna coupler to capture or inject the WiFisignal from/to the built-in antenna.Signal amplifier to compensate for the losscaused by the back cover of the phone.  

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