Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. License
5. Contact

Coalitional Fairness of AVs at T-Intersection

Paper available at https://example.com

What:

We analyze the fairness of actions by Autonomous Vehicles (AVs) as they act in the self-interest of their coalition, or company, by comparing the changes in efficiency at a T-Intersection with and without a fairness regulation.

Why:

Although AV companies may create decision making algorithms with the intention of improving the performance of their vehicles, it may cause cause inefficiencies in traffic and potentially create unfair situations with other vehicles.

How:

TODO

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

• npm

  npm install npm@latest -g

Installation

Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.

1. Get a free API Key at https://example.com
2. Clone the repo

   git clone https://github.com/your_username_/Project-Name.git

3. Install NPM packages

   npm install

4. Enter your API in config.js

   const API_KEY = 'ENTER YOUR API';

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Usage

Optimal_Path.py uses the Q-Table from training to make decision and will output the group exiting time data for all queue configurations (num = 1-11) formatted as queue_{num}.npy or queue_f_{num}.npy with fairness reward.

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Contact

Diana Gomez - [email protected]

Project Link: https://github.com/dianaggomez/coalitional_fairness

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