Here's the updated README.md based on the provided Flowsim paper and the existing README.md file:
Flowsim is a novel simulator specifically designed to evaluate the behavior and data flow of connected autonomous vehicles (CAVs) in large-scale environments. It offers a modular and extensible architecture that enables the analysis of CAV behaviors in city-scale scenarios.
Flowsim is written in pure Python within approximately 1,500 lines of code, making it highly readable, understandable, and easily modifiable. The purpose is to enable researchers to fast-prototype, simulate, and test CAV algorithms and functions. By implementing the desired modules and defining flow between them, users can create custom vehicle types very easily.
The key features of Flowsim are:
- City-scaled: Flowsim provides a city-scale experimental environment for evaluating CAV behaviors in realistic traffic scenarios.
- Extensible: Flowsim offers a modular and extensible architecture that allows easy integration of new simulation components and customization of vehicle types and behaviors.
- Flexible: Flowsim enables researchers to define custom data flows and implement desired modules to create custom vehicle types and analyze their interactions.
- High-performance: Flowsim achieves high-performance simulation capabilities, enabling efficient evaluation of large-scale scenarios with thousands of vehicles.
- Reproducible: Flowsim ensures reproducibility of experiments by providing a well-documented and accessible codebase, fostering collaboration and knowledge sharing within the research community.
Flowsim consists of three major components: Environment, Sandbox, and Vehicles.
- Environment: The environment component includes various simulators such as PositionManager, NetworkSimulator, PerceptionSimulator, and MatchSimulator. These simulators provide the necessary information and functionality for evaluating CAV behaviors.
- Sandbox: The sandbox component ensures the legitimacy of vehicle implementations by explicitly controlling the information accessible to agents and the operations they can perform.
- Vehicles: Flowsim supports different types of vehicles, including general-purpose vehicles, V2X-connected vehicles, Proof-of-Traffic (PoT) vehicles, and attacker vehicles with tampered OBU implementations. The data flow among vehicle modules is represented by a Directed Acyclic Graph (DAG).
If you are using the Flowsim framework for your research or development, please cite the following paper:
Y. Tao, E. Javanmardi, J. Nakazato, M. Tsukada and H. Esaki, "Flowsim: A Modular Simulation Platform for Microscopic Behavior Analysis of City-Scale Connected Autonomous Vehicles," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 2186-2193, doi: 10.1109/ITSC57777.2023.10421900.
BibTeX entry:
@INPROCEEDINGS{10421900,
author={Tao, Ye and Javanmardi, Ehsan and Nakazato, Jin and Tsukada, Manabu and Esaki, Hiroshi},
booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)},
title={Flowsim: A Modular Simulation Platform for Microscopic Behavior Analysis of City-Scale Connected Autonomous Vehicles},
year={2023},
volume={},
number={},
pages={2186-2193},
keywords={Codes;Protocols;Transportation;Behavioral sciences;Computer security;Autonomous vehicles;Software development management},
doi={10.1109/ITSC57777.2023.10421900}
}TBD
We welcome your contributions to Flowsim.
- Please report bugs and suggest improvements by submitting issues on the GitHub repository.
- Submit your contributions using pull requests.
- Ensure your code follows the established coding style and conventions used in the Flowsim codebase.
- Provide clear and concise descriptions of your changes, along with any necessary documentation updates.