Solutions for acceleration measurement in vehicle crash tests

Dima, Dragoş Sorin; Covaciu, Dinu

doi:10.1088/1757-899x/252/1/012007

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…These sensors have their configurations and threshold range. The accelerometer's input range can be 2g to 200g (negative and positive) and it can vary even more [23]. Whereas, the vibration sensor has only two states, low and high.…”

Section: Proposed Methodsmentioning

confidence: 99%

IoT based car accident detection and notification algorithm for general road accidents

Sharma

Sebastian²

2019

IJECE

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<p>With an increase in population, there is an increase in the number of accidents that happen every minute. These road accidents are unpredictable. There are situations where most of the accidents could not be reported properly to nearby ambulances on time. In most of the cases, there is the unavailability of emergency services which lack in providing the first aid and timely service which can lead to loss of life by some minutes. Hence, there is a need to develop a system that caters to all these problems and can effectively function to overcome the delay time caused by the medical vehicles. The purpose of this paper is to introduce a framework using IoT, which helps in detecting car accidents and notifying them immediately. This can be achieved by integrating smart sensors with a microcontroller within the car that can trigger at the time of an accident. The other modules like GPS and GSM are integrated with the system to obtain the location coordinates of the accidents and sending it to registered numbers and nearby ambulance to notify them about the accident to obtain immediate help at the location.</p>

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Section: Proposed Methodsmentioning

confidence: 99%

IoT based car accident detection and notification algorithm for general road accidents

Sharma

Sebastian²

2019

IJECE

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“…Konsep pengujian benturan yang dilakukan adalah pengidentifikasi berdasarkan perubahan pada percepatan objek yang dilakukan secara tiba-tiba dalam ukuran g-force yang besar, umumnya 4G [6]. Nilai akselerasi ini digunakan sebagai dasar untuk mengidentifikasi kejadian tabrakan, perekam data pada sumbu x dan y dari sensor akselerometer [7]. Parameter yang dibutuhkan adalah penentuan titik lokasi kejadian yang dapat diketahui melalui implementasi GPS dengan mengekstrak variabel latitude dan longitude.…”

Section: Faktor Pendukungunclassified

Aplikasi Pengidentifikasian Kecelakaan Kenderaan Bermotor Menggunakan Internet Of Things ( Iot )

Simorangkir

Malabay²,

Patasik³

2022

Technologia

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Tingkat pertambahan kenderaan bermotor dengan pertambahan jalan tidak berimbang yang mengakibat sering terjadi kemacetan dan bahkan kecelakaan. Faktor penyebab kecelakaan tersebut adalah para pengendera merasa lelah selama bekerja, kebutuhan waktu yang mendesak atau pengendera yang lain tidak sadar dalam mengenderai kenderaannya yang mengakibatkan orang lain berdampak. Berdasarkan informasi tersebut perlu dirancang sebuah sistem yang berbasis Internet Of Things (IoT), serta komponen-komponen yang diperlukan dalam merancang Prototype tersebut. Dengan percobaan pengujian sistem pintar ini memberikan akurasi 73% dengan kecepatan memberikan respon ke pos penanggulangan kecelakaan adalah lebih kurang 6 detik dan tempat kejadian dapat dengan segera diketahui, agar pengendera dapat segera tertolong dari kondisi tersebut.

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“…In general, vehicle vibrations and electromagnetic interference generate noise and other unwanted signal components in the inertial signal samples. erefore, it is necessary to select the appropriate signal filtering technique to minimize the noise and maximize the signal [26]. e acceleration data of the simulation test were filtered with a CFC60 SAE filter.…”

Section: Correlation Model Between the Velocity Variation Error And Influencing Factorsmentioning

confidence: 99%

Method to Correct the Velocity Variation Information of an Automatic Crash Notification System in Vehicle‐to‐Rigid Barrier Frontal Collisions

Lü

Shu

2021

Shock and Vibration

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Automatic crash notification systems (ACNSs) play a key role in post-accident safety. To improve the accuracy and efficiency of ACNSs, a method to correct the velocity variation information of ACNSs was established. First, after the acceleration data of sled crash tests were analysed, the factors affecting the accuracy of the velocity variation information were determined, and the influence of the discrimination threshold and acceleration curve shape on the velocity variation information was examined. Second, according to the acceleration data generated by the simulation model of a sled crash, the correlation between the accuracy of the velocity variation information and influencing factors was modelled. Third, an automatic crash notification algorithm involving a velocity variation correction function (VVCF) was proposed based on the correlation model. Finally, to verify its reliability, the improved algorithm was applied to an automatic crash notification system (ACNS) terminal. The validation results show that the ACNS terminal can accurately identify collisions and transmit accident information. Moreover, more accurate velocity variation information can be retrieved.

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Solutions for acceleration measurement in vehicle crash tests

Cited by 6 publications

References 1 publication

IoT based car accident detection and notification algorithm for general road accidents

IoT based car accident detection and notification algorithm for general road accidents

Aplikasi Pengidentifikasian Kecelakaan Kenderaan Bermotor Menggunakan Internet Of Things ( Iot )

Method to Correct the Velocity Variation Information of an Automatic Crash Notification System in Vehicle‐to‐Rigid Barrier Frontal Collisions

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