As connected autonomous vehicles (CAVs) become increasingly prevalent, there is a growing need for simulation platforms that can accurately evaluate CAV behavior in large-scale environments. In this paper, we propose Flowsim, a novel simulator specifically designed to meet these requirements. Flowsim offers a modular and extensible architecture that enables the analysis of CAV behaviors in large-scale scenarios. It provides researchers with a customizable platform for studying CAV interactions, evaluating communication and networking protocols, assessing cybersecurity vulnerabilities, optimizing traffic management strategies, and developing and evaluating policies for CAV deployment. Flowsim is implemented in pure Python in approximately 1,500 lines of code, making it highly readable, understandable, and easily modifiable. We verified the functionality and performance of Flowsim via a series of experiments based on realistic traffic scenarios. The results show the effectiveness of Flowsim in providing a flexible and powerful simulation environment for evaluating CAV behavior and data flow. Flowsim is a valuable tool for researchers, policymakers, and industry professionals who are involved in the development, evaluation, and deployment of CAVs. The code of Flowsim is publicly available on GitHub under the MIT license.

翻译：随着网联自动驾驶车辆（CAV）日益普及，亟需能够在大规模环境下精准评估CAV行为的仿真平台。本文提出Flowsim——一种专为满足此类需求而设计的新型仿真器。Flowsim采用模块化可扩展架构，支持大规模场景下CAV行为的分析，为研究人员提供可定制的平台，用于研究CAV交互、评估通信与网络协议、分析网络安全漏洞、优化交通管理策略，以及制定和评估CAV部署方案。Flowsim采用纯Python实现，代码量约1500行，具有高度可读性、易理解性和易修改性。我们通过一系列基于真实交通场景的实验验证了Flowsim的功能与性能。结果表明，Flowsim能够为评估CAV行为与数据流提供灵活高效的仿真环境，对参与CAV开发、评估与部署的研究人员、政策制定者及行业专家均具有重要价值。Flowsim的代码已在GitHub上以MIT许可证公开。