Blind Zone Area Calculation

Description

This code repository is part of Olin College of Engineering's 2023-2024 Santos-Volpe SCOPE project.

The purpose of this code is to calculate the blind zone area of a vehicle, given a dataset of nearest visible points (NVPs) for that vehicle.

Setup

Dependencies

• Matplotlib
• Numpy
• pandas

Adding Data

1. Save NVP data in a csv file. The data should be in cartesian coordinates, formatted into two columns labeled x (ft) and y (ft).
2. Add the csv file into the Data folder located in the root level of this repository.
3. See the following section for loading the data.

Usage

Add code to or modify the following files and functions to get a visualization of your NVPs and a value for the blind zone area.

1. In data_processing.py, modify the function load_data.

   • In this function, there are code blocks with structure similar to the following. These are used to load and format NVP data from a csv file and compare them. Modify this function as needed to load the correct datasets. An example template is shown below; replace any all-caps variables with your own.

     # load NVPs
     DATA_raw = pd.read_csv(DATA_FILEPATH)
     # extract data from correct columns
     DATA_nvps = DATA_raw[["x (ft)", "y (ft)"]].to_numpy()
     # append columns with polar coordinates
     DATA_nvps = np.concatenate((DATA_nvps, cart_to_polar(DATA_nvps)), axis=1)
     # sort by angle - largest to smallest
     DATA_nvps = DATA_nvps[DATA_nvps[:, 3].argsort()[::-1]]

     Note: More complex data filtering can be done; see pandas documentation or other code blocks in this function for more examples.

   • At the end of this function, modify the variable datasets to include only the datasets of interest.

2. In compare_nvp_area.py modify the function main.

   • Modify the variable labels so that the number and order of the labels match the number and order of the datasets returned from load_data.

   • If there is a ground truth dataset included in the list of datasets, make sure that its label contains the string "Ground Truth" to ensure that it is recognized as a ground truth dataset.

3. Depending on your Python version, run one of the following commands in the terminal at the root level of this repository to run the code.

   python3 compare_nvp_area.py

   python compare_nvp_area.py