Project Design

Objectives

"map bicycle lanes & suggest the fastest bicycle or electric kickboard path"
In order to achieve it, these modules should be developed (see #1 Modules) :
- a ROS package for traffic sign recognition
- a bicycle path mapper
- traffic sign estimation system
- shortest path suggestion

While riding a bicycle or an electric kickboard, assume that every vehicle has front-view camera (or a smartphone) which can detect pedestrian traffic lights. Let's call it an edge camera.
An android smartphone would work as an edge device at the beginning.
The camera is turned off in general.
The camera starts to record the view when the vehicle is near intersection based on GPS coordinate.
Send a frame, the orientation, the compass data, time, and the GPS coordinate per a second until an edge server tells it to stop capturing.

An edge server is a type of edge device that provides an entry point into a network.
A Nvidia Jetson nano would work as an edge device at the beginning.
The edge server receives every data from edge cameras.
It processes the images to a boolean value if the pedestrian traffic light is green or not.
When the boolean value changed from red to green, it means the signal edge arises. Send a stop signal to an edge camera in every signal rising edge.

ROS is applied to make a stable data publisher/subscriber design, and it also provisions multi-platform device services.
An edge camera sends images without interpreting them. An edge server interprets them with image processing. This design will help edge cameras to save their battery & will help to process the images faster using CUDA.
A backend server shouldn't know of this package.
Used frameworks & libraries:
- An edge camera
- ROS1 (Melodic) as a publisher
- An edge server
- ROS1 (Melodic) as a subscriber (roscore)
- OpenCV for image processing

an alternative way to get images using smartphones, which also sends images without interpreting them.
Used frameworks & libraries:
- An edge camera
- Nativescript-Vue
- Android API level ^21
- An edge server
- RESTful web service
- OpenCV for image processing

A bicycle path mapper maps intersections & roads to a graph using Kakao Map API.
A backend server does image processing because the API doesn't provide conceptual mapping data or a graph.
Used frameworks & libraries:
- A backend server
- Kakao Map API
- OpenCV for image processing
When an edge camera sends its GPS coordinates via an edge server, a bicycle path mapper returns intersections & roads neighborhood.

Every single data of an edge camera sends images to a backend server via an edge server. This will help machine learning model more accurate.
Hope to guess the accurate timing of traffic lights only within hundreds of images. It is possible because a single ride of a bicycle captures not only a single status of a traffic light, but also the changing from red to green.
Used frameworks & libraries:
- A backend server
- PyTorch
- numpy
- pandas
- keras
- postgreSQL

A backend server suggests an edge camera where to go by based on the estimated timing of traffic lights & current position of the bicycle path map.
Assume that the delay of traffic lights of intersection is much larger than traffic jam. It's very down to Earth assumption because we can hardly see a traffic jam on bicycle lane!
Selecting the best shortest path algorithm will be the issue.