Wireless Black Box Using Rasberry Pi

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Description

Nowadays, traffic accidents are one of the main reasons for fatalities in the world, many lives can be saved by reducing the time between the occurrence of an accident and the arrival of first help at its location. In the research literature, numerous accident detection and notification systems were proposed to solve this problem. Including built-in units, black-box, and mobile phone-based solutions. Our research has been devoted to designing a black-box system that integrates IoT and Global Positioning System (GPS) technologies to locate and send a Minimum Set of Data (MSD) containing important information about the accident collected using different sensors such as the accident type, longitude, and latitude, time and speed of the vehicle to the emergency services (Hospitals, civil protection, and police). This system is developed based on the Raspberry Pi 3 in order to provide both low-cost and high system performances.


INTRODUCTION

In the present situation, any accident occurs the information about an accident is needed to find out the cause of the accident. In this case, the investigators should know about the accident at that time the Black box is more useful. Then the investigators should easily know about the accident. In case of any accident occurs immediately location and message should be sent to the ambulance and rescue team. Then they should easily know about the accident. The information about the accident is gathered from the Black box. It can store information about the vehicle and surrounding images also. Previously it can be used in Helicopters and Airplanes. Now we are trying to implement it in our own vehicles. If any accident occurs, the message will send to the provided mobile numbers. Various sensors are used to build the black box in order to find the speed of the vehicle, pressure, and angle detection. If the angle of the vehicle will be changed then the sensor will detect the accident immediately motor will stop. It continuously records information like the speed of the vehicle, and sensor values surrounding images and stores them in the internal memory. Various sensors are used to build the black box in order to find the speed of the vehicle, pressure, and angle detection. If the angle of the vehicle will be changed then the sensor will detect the accident immediately motor will stop.


Existing system

  • Post-Accident Detection Systems.
  • Lack of Intelligence in the detection systems.
  • Fails to track the collision and pre-damage status.

DISADVANTAGE

  • Uses the internet for the alert.
  • Accuracy is low.
  • Limited since it needs a manual button to reduce faulty notification.

Proposed system

In this project, the Black Box is trying to implement in vehicles. The vehicles should have a Black Box system so, that when an accident occurs the information should be stored in that system. The sensors can detect accidents and sometimes they should avoid the accidents. In this, we are using IoT functionality than by using the internet also we can able to share the data.

ADVANTAGES

  • GPS tracking is easy and very fast.
  • The vehicle number and person’s contact number will be transferred to the police control room is immediate.
  • Locking systems are automatic.

BLOCK DIAGRAM

Wireless Black Box Using Rasberry Pi


CIRCUIT DIAGRAM:

black box


BLOCK DIAGRAM DESCRIPTION

In this proposed device, a Wireless black box is basically a device that will indicate all the parameters of a vehicle crash and will also store and display its parameters every three seconds such as date, time, IR, MEMS, location, vibration, alcohol limit, etc. At the time of the accident, the message will be sent from the system built inside the car to the registered mobile numbers such as IoT Technology of police stations, hospitals, family members, owners, etc. We have used various types of sensors like Vibration sensors to measure vibrations felt by the car during accidents. Alcohol sensors are located on the steering wheel which will indicate whether the driver is drunk. An accident occurs at that time if any angle changes that will be detected by the MEMS sensor. Distance between two vehicles is less than the minimum distance then the Ultrasonic sensor will detect it.IR sensor is connected to the car in the wheel part to measure the speed of the car as in a simple? manner. All the parameters sensed by the sensors will send the signal to Raspberry pi. If any accident occurs sensor will detect it and IOT functionality for send the GPS latitude and longitude values.


HARDWARE REQUIREMENTS

  • Raspberry Pi
  • IR Sensor
  • MEMS Sensor
  • Ultrasonic sensor
  • Vibration sensor
  • Alcohol sensor
  • DC Motor
  • Motor driver
  • GPS
  • MPC3008(ADC)

SOFTWARE REQUIREMENTS

  • Program: Python
  • Platform: Python 3 IDLE
  • Raspberry pi os: Raspian os
  • Library: OpenCV

REFERENCES

  1. Meena, A., Iyer, S., Nimje, M., Jogjekar, S., Jagtap, S., Rahman, M.: Automatic accident detection and reporting framework for two-wheelers. In: IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 962? 967 (2014).
  2. Dae Geun, J. Se Myoung, L. Myoung Seob, ?System on Chip design of Embedded Controller for Car Black Box?, Intelligent Vehicles Symposium IEEE 2007, pp. 1174-1177, 13 June 2007.
  3. Bony? r, A., G?czy, A., Krammer, O., S? the, H., Ill’s, B., K?m?n, J.: A review on current eCall systems for autonomous car accident detection. In: 40th International Spring Seminar on Electronics Technology (ISSE), pp. 1?8 (2017)
  4. Megalingam, R.K., Nair, R.N., Prakhya, S.M.: Wireless vehicular accident detection and reporting system. In: 2010 International Conference on Mechanical and Electrical Technology (ICMET 2010), pp. 636? 640 (2010)
  5. Kremonas, P., P? ris, J.: Everything you wanted to ask, but did not know how?. In: The European Emergency Number Association (EENA) (2015)
  6. White, J., Thompson, C., Turner, H., Dougherty, B., Schmidt, D.C.: WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones. In: Springer Science+Business Media, LLC, 285?303 (2011).

 

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