Week 3 - Sep 18 - 24

 

                         Below is the overall layout/ plan of the traffic predictor project

Plan

Module 1: Traffic prediction with machine learning on an added advantage of mass storage capability of the STM32 Nucleo-64 development board

 

Components:

HardwareSoftware
Nucleo L476RG boardMbed Compiler/ VS Code
GPS sensorGMaps API
Wi-Fi Expansion boardMATLAB & Arduino (Mobile Application)

 

This holds the key functionality of the project. Data will be collected from user such as,

  1. Location
  2. Time
  3. Traffic

The collected data is machine learned to develop a pattern with time & traffic in a particular location. This will help users avoid traffic.

 

Module 2: Auto-pilot mode with predefined speed using Sensor Expansion board

 

Components: Module1's components +

Hardware
Sensor Expansion board

 

While on traffic one would easily get bored up. Enabling an auto-pilot mode with a predefined speed from data collected & real-time feed will help one relax.

 

Module 3: Speed adjustment with correspondence to current vehicular movement and real-time traffic

 

Components: Module1, 2's components +

Hardware
Camera/CCTV
IR sensor

The real-time feed is obtained through calculating the position of other vehicles from our vehicle. This is used for adjusting the speed of the vehicle.

 

Please find below links to my previous blogs on my traffic predictor project for IoT on Wheels design Challenge,

Blog 1 - The official announcement

Blog 2 - Quest for the code editor

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