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MindMap Gallery Vehicle Position and Speed Measurement with Radar and Camera Modules

Vehicle Position and Speed Measurement with Radar and Camera Modules

This is a mindmap about a project focused on vehicle position and speed measurement using radar and wide-angle cameras. The diagram highlights the stages of the project, including the initial implementation of radar and camera integration with the Raspberry Pi, followed by object detection, positioning, and speed measurement using radar and camera data. It also emphasizes technical documentation, including the use of Python for signal processing, FFT, and peak detection.

Edited at 2025-01-23 18:19:46

Shayma

Recent works View more works>>

Vehicle Position and Speed Measurement with Radar and Camera Modules
This is a mindmap about a project focused on vehicle position and speed measurement using radar and wide-angle cameras. The diagram highlights the stages of the project, including the initial implementation of radar and camera integration with the Raspberry Pi, followed by object detection, positioning, and speed measurement using radar and camera data. It also emphasizes technical documentation, including the use of Python for signal processing, FFT, and peak detection.

Vehicle Position and Speed Measurement with Radar and Camera Modules

Shayma

Recent works View more works>>

Vehicle Position and Speed Measurement with Radar and Camera Modules
This is a mindmap about a project focused on vehicle position and speed measurement using radar and wide-angle cameras. The diagram highlights the stages of the project, including the initial implementation of radar and camera integration with the Raspberry Pi, followed by object detection, positioning, and speed measurement using radar and camera data. It also emphasizes technical documentation, including the use of Python for signal processing, FFT, and peak detection.

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Vehicle Position & Speed Measurement Using Radars, Wide-Angle Cameras

Stage 1: Project Planning

Create the requirements elicitation document.

Create the work breakdown structure of the project.

Create the Dependency Matrix, Gantt Chart, and Networking Diagram.

Stage 2: Initial Research

Organize a side task to (1) learn the principles of radar and (2) programming using Python.

Gather research papers relevant to our SDP topic and write notes.

Gather online tutorials on using Python for radar applications and compile them into a document.

Stage 3: Initial Implementation

Create a matrix to analyse and study the features of multiple radar and camera modules candidates, and select one.

Generate a table with the components and equipment needed. Include the cost, number of items needed, and the links to the seller website.

Order the items.

Interface the radar with the Raspberry Pi through the UART communication protocol. Produce a document on the procedure, setup, and testing code (that the Raspberry Pi recognizes the module and can receive its data).

Interface the camera with the Raspberry Pi. Produce a document on the procedure, setup, and testing code (that the Raspberry Pi recognizes the module and can receive its data)

Stage 4.a: Object Detection, positioning, and Speed Measurment Using the Radar Module

Search Python libraries for signal processing.

Study or write the python code to perform 3D FFT on the raw data to obtain the 2D radar tensors (2D Range – Azimuth, 2D Range- Doppler), and the 3D radar tensor (RAD) to obtain the position and speed measurement of the detected vehicle.

Write or study the Python code to perform a peak detection method such as CFAR (constant false alarm rate) to convert the data to the point clouds form.

Use grid – based methods (grid mapping) to map the point clouds into a 2D plane.

Perform the clustering algorithm (such as DBSCAN) for object detection.

Produce a technical document explaining the clustering algorithm and other possible methods to detect objects using data in the point clouds form.

Produce a technical document explaining the peak detection methods, and the CFAR.

Produce a technical document explaining the Fast Fourier Transform, how it is used to obtain information regarding the doppler velocity and range. Discuss other less efficient alternatives.

Stage 4.b: Object Detection Using the Camera Module

Search relevant Python libraries for processing the camera data.

Apply the Background Subtraction Algorithm (BSA) to detect objects.

Stage 5: Software Development for Visulization

Integrate the codes of both modules.

Write or study a Python code to display position, speed, and category of the object detected by the radar.

Write or study a Python code to display the category of the object detected by the camera.

Stage 6: Hardware System Design and Implementation

Generate 3 possible designs of the hardware system.

Generate a matrix to analyze and select one design.

List and order the components and equipment needed for the design.

Implement the selected model.