Metadata-Version: 2.1
Name: sdriving
Version: 0.1.0
Summary: Design multi-agent environments and simple reward functions such that social driving behavior emerges
Home-page: https://github.com/fidler-lab/social-driving/
Author: Avik Pal
Author-email: avikpal@cse.iitk.ac.in
License: MIT
Description: <div align="center">
        
        # Social Driving
        
        **Emergent Road Rules In Multi-Agent Driving Environments**
        
        [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
        [![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://avikpal.gitbook.io/social-driving/)
        [![License](http://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat)](LICENSE)
        [![PyPI version](https://badge.fury.io/py/sdriving.svg)](https://badge.fury.io/py/sdriving)
        [![Website](https://img.shields.io/badge/wepage-up-red.svg)](https://fidler-lab.github.io/social-driving/)
        [![arXiv](https://img.shields.io/badge/arXiv-2011.10753-f9f107.svg)](https://arxiv.org/abs/2011.10753)
        [![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Ffidler-lab%2Fsocial-driving&count_bg=%23C83D3D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com)
        
        <img src="assets/header.gif">
        
        </div>
        
        For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow. "Road rules" include rules that drivers are required to follow by law -- such as the requirement that vehicles stop at red lights -- as well as more subtle social rules -- such as the implicit designation of fast lanes on the highway. In this paper, we provide empirical evidence that suggests that -- instead of hard-coding road rules into self-driving algorithms -- a scalable alternative may be to design multi-agent environments in which road rules emerge as optimal solutions to the problem of maximizing traffic flow. We analyze what ingredients in driving environments cause the emergence of these road rules and find that two crucial factors are noisy perception and agents' spatial density. We provide qualitative and quantitative evidence of the emergence of seven social driving behaviors, ranging from obeying traffic signals to following lanes, all of which emerge from training agents to drive quickly to destinations without colliding. Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving. 
        
        
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        ## Installation
        
        Stable Release version is available through pip
        
        ```
        pip install sdriving
        ```
        
        Alternatively you can install using the latest (unreleased) version using
        ```
        pip install git+https://github.com/fidler-lab/social-driving.git
        ```
        
        
        ## Nuscenes Maps
        
        Preprocessed [nuScenes Maps](https://www.nuscenes.org/) are provided [here](https://github.com/fidler-lab/social-driving/releases/download/v0.0.1-alpha/data.zip), with the tagged release of this software. If you use these maps in your research consider citing the [nuScenes Paper](https://arxiv.org/abs/1903.11027).
        
        ## Questions/Requests
        
        Please file an issue if you have any questions or requests about the code or the paper. If you prefer your question to be private, you can alternatively email me at avikpal@cse.iitk.ac.in.
        
        ## Citation
        
        If you found this codebase useful in your research, please consider citing
        
        ```
        @misc{pal2020emergent,
            title={Emergent Road Rules In Multi-Agent Driving Environments}, 
            author={Avik Pal and Jonah Philion and Yuan-Hong Liao and Sanja Fidler},
            year={2020},
            eprint={2011.10753},
            archivePrefix={arXiv},
            primaryClass={cs.LG}
        }
        ```
        
Keywords: deep learning,pytorch,RL,AI
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Natural Language :: English
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
