Week 3 - Sep 18 - 24
Below is the overall layout/ plan of the traffic predictor project
Module 1: Traffic prediction with machine learning on an added advantage of mass storage capability of the STM32 Nucleo-64 development board
|Nucleo L476RG board||Mbed Compiler/ VS Code|
|GPS sensor||GMaps API|
|Wi-Fi Expansion board||MATLAB & Arduino (Mobile Application)|
This holds the key functionality of the project. Data will be collected from user such as,
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 +
|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 +
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,