{"id":4426,"date":"2020-04-22T22:32:39","date_gmt":"2020-04-23T02:32:39","guid":{"rendered":"https:\/\/www.hajim.wdev.rochester.edu\/senior-design-day\/emergency-vehicle-alert-e-v-a\/"},"modified":"2020-05-05T18:58:26","modified_gmt":"2020-05-05T22:58:26","slug":"emergency-vehicle-alert-e-v-a","status":"publish","type":"post","link":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/emergency-vehicle-alert-e-v-a\/","title":{"rendered":"Emergency Vehicle Alert (E.V.A.)"},"content":{"rendered":"\n

Team Members<\/h2>\n\n\n\n

Sylvester Benson-Sesay
Phuc Do
Gabriel Sarch<\/p>\n\n\n\n

Supervisors<\/h4>\n\n\n\n

Ross Maddox, Ph.D., Biomedical Engineering, University of Rochester<\/p>\n\n\n\n

Customers<\/h4>\n\n\n\n

Marlene Sutliff and Steven Barnett, UR Community\/Deaf Wellness Center and Daniel Brooks, President HLAA NYS Association<\/p>\n\n\n\n

Description<\/h2>\n\n\n\n

There is a need to ensure that drivers are alerted of approaching emergency vehicles so that they can remove themselves from the path of the emergency vehicle. It is especially a challenge for deaf, hard of hearing, and distracted drivers to identify emergency signals, which puts them at an increased risk for collision. People that are hard of hearing and deaf people are three times as likely to be involved in a motor vehicle accident and up to nine times as likely to be seriously injured in the accident (National Academy of Forensic Engineer, 2016). In this project, we developed a device for use in the car that detects emergency vehicles and notifies the driver of their presence in real-time. We trained a convolutional neural network to detect sirens in noisy environments. A demonstration of our real-time detector and design schematic is shown below.<\/p>\n\n\n\n

\"System
The figure above shows the system levels of this device. Four systems that work independently of each other but when put together give a continuous flow of events. In the first level, there is a hardware-software interaction. The microphone records the traffic soundscape in real-time. This audio is stored in a buffer that breaks the audio into 3-second chunks. In the second system, the 3-second chunks are sent to the trained convolutional neural network that gives a probability of whether a siren is present or not. The third system-level uses the probability and compares it to a set threshold which inputs a boolean of a siren or no siren present. The fourth system uses this boolean to trigger the output general-purpose input\/output (GPIO)<\/figcaption><\/figure><\/div>\n\n\n\n

Assembly<\/h2>\n\n\n\n
\"\"
Figure above shows the set up of the device from the left side. The Mic detects the siren from the emergency vehicle, causing the lights on the Raspberry Pi to blink green in rotation to warn drivers.<\/figcaption><\/figure>\n\n\n\n
\"\"
Figure above shows the set up of the device from the right side. The Power Bank and the Raspberry Pi are both held by the GPS holder to keep them stable during traffic.<\/figcaption><\/figure>\n\n\n\n

Features<\/h2>\n\n\n\n
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  • Raspberry Pi 3<\/li>
  • USB Microphone<\/li>
  • Power Bank<\/li>
  • LED Light Case<\/li>
  • USB to Micro USB Cable<\/li><\/ul>\n<\/div><\/div>\n\n\n\n
    \n
    <\/div>\n<\/div>\n\n\n\n
    \"\"
    Schematic of the core of the device, raspberry pi 3, with its peripheral input components labelled
    *Clipart Raspberry<\/figcaption><\/figure><\/div>\n\n\n\n

    Detection Software<\/h2>\n\n\n\n

    Software Demonstration Video<\/strong><\/p>\n\n\n\n

    Below is a video demonstrating our real-time detector while playing various types of noise. <\/p>\n\n\n\n

    \n