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🚗 immitation-learning - A Safer Approach to Autonomous Driving

🌟 Overview

TSAC-DE is a project designed to create a safe reinforcement learning algorithm for autonomous driving. This application focuses on ensuring safety while making real-time driving decisions. It combines advanced techniques to help self-driving cars learn effectively and operate safely in complex environments.

Key Features

Spatio-temporal Transformer Encoder: This component helps analyze sequential driving data, allowing the system to predict and adapt to changing conditions on the road.
Deep Ensemble Critics: Multiple models work together to provide precise estimates of uncertainty in decision-making, enhancing safety.
Trust Score: This score guides safe decisions based on how much confidence the system has in its chosen actions.
Control Barrier Functions (CBFs): These functions ensure the vehicle meets safety constraints, reducing risks while driving.

🚀 Getting Started

Follow these steps to download and run TSAC-DE on your machine.

Step 1: Visit the Releases Page

To get started, go to our Releases page. Here, you can find the latest version of the software.

Step 2: Download the Application

On the Releases page, look for the most recent version. Click on the link that corresponds to your operating system. This will usually be a file you can download directly, such as .exe for Windows or .tar.gz for Linux.

Step 3: Install the Application

Once the download is complete, locate the file in your downloads folder:

For Windows: Double-click on the .exe file and follow the on-screen instructions to install the software.
For Linux: Open a terminal, navigate to the download directory, and extract the files using the command:
```
tar -xzvf your-file-name.tar.gz
```
Then enter the extracted folder and follow the README instructions included there.

Step 4: Run the Application

After installation, you can run TSAC-DE:

On Windows, find the program in your Start Menu and click to open it.
On Linux, navigate to the directory where you extracted the files and run:
```
./your-application-name
```

📊 Project Structure

The repository includes the following main directories:

tsac-de/
├─ tsac_de/          # Core package containing agents, models, safety checks, and utilities
├─ configs/          # Contains experiment configurations in YAML format
├─ carla/            # Includes wrappers for the CARLA simulator

This structure helps keep the project organized, ensuring each component is easy to find.

🖥️ System Requirements

Before running the software, ensure your system meets the following requirements:

Operating System: Windows 10 or later, or a recent version of Linux.
Processor: Multi-core processor with at least 2 GHz speed.
Memory: At least 8 GB of RAM.
Storage: Minimum of 2 GB of free disk space.
Graphics Card: A dedicated graphics card for optimal performance, though integrated graphics may also work.

📥 Download & Install

To start using TSAC-DE, visit our Releases page to download the application. Follow the installation instructions listed above. If you encounter any issues during your installation or running the software, please consult the FAQs section below.

❓ FAQs

How do I report an issue?

If you find a bug or have feedback, please create an issue on the GitHub repository. Your input helps us improve the project.

Can I contribute to this project?

Yes! We welcome contributions. Please refer to the CONTRIBUTING.md file in the repository for guidelines on how to contribute.

Where can I find more documentation?

Additional documentation is available in the docs/ folder of the repository. This includes detailed explanations of the algorithms and libraries used in this project.

Is there support for other platforms?

Currently, the application is primarily designed for Windows and Linux. Mac support may come in future releases depending on community interest.

📞 Contact

For support or questions, please reach out via GitHub issues, and we will get back to you promptly.

Thank you for your interest in TSAC-DE! Enjoy exploring the world of safe reinforcement learning.