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Code for Bachelor's thesis: Observation of humans using computer vision for autonomous driving

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raquelpanapalen/2DCyclistTrajectoryPrediction

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2DCyclistTrajectoryPrediction

Install

The requirements.txt file contains a list of all the necessary packages and their versions for this project.

git clone [email protected]:raquelpanapalen/2DCyclistTrajectoryPrediction.git
cd 2DCyclistTrajectoryPrediction
pip install -r requirements.txt

Project structure

This repository contains scripts and notebooks for the project. Below is an overview of the directory structure and contents:

- scripts/
    - preprocessing/          # Scripts for data preprocessing
    - evaluation/             # Scripts for model evaluation
    - models/                 # Scripts for model architectures
    - trainers/               # Scripts for model training
    - visualization/          # Scripts for data visualization
    - utils.py                # Utility functions script
    - metrics.py              # Metrics computation script
    
- notebooks/                  # Jupyter notebooks

Scripts

The scripts directory serves as the main folder for all the project's scripts. It is organized into subfolders based on their specific functionalities:

  • preprocessing/: This folder contains scripts responsible for data preprocessing tasks. These scripts handle data extraction from Google Cloud Storage, data formatting, normalization, and cleaning.

  • evaluation/: Here, you can find scripts dedicated to model evaluation. These scripts compute various evaluation metrics, and generate performance reports.

  • models/: The models folder contains scripts related to the definition and implementation of model architectures. These scripts include the code for building and configuring different models used in the project.

  • trainers/: In this folder, you will find scripts responsible for model training. These scripts handle the training process, including data loading, validation steps, and saving trained models.

  • visualization/: This folder contains scripts for data visualization. These scripts generate plots, graphs, and other visual representations of the data and model outputs.

  • utils.py: This script contains utility functions that are commonly used across different parts of the project. It includes helper functions, data manipulation tools, and other general-purpose utilities.

  • metrics.py: This script provides functions for computing various metrics used for evaluating model performance.

Notebooks

The notebooks directory contains Jupyter notebooks used for interactive data analysis, experimentation, and documentation purposes. These notebooks provide a user-friendly environment for exploring the data, visualizing results, and documenting the research process.

Feel free to navigate through the directories and explore the code and notebooks to gain a deeper understanding of the project. Happy exploring!

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Code for Bachelor's thesis: Observation of humans using computer vision for autonomous driving

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