开源单目视觉仿真平台，助力低成本自动驾驶车研究与教学
MonoSIM: An open source SIL framework for Ackermann Vehicular Systems with Monocular Vision
- 基于单目摄像头与滑动窗口车道检测，降低计算开销
- 验证了MPC与PID算法在平台上的可靠表现
- 适合教育、低预算AGV开发及多智能体系统研究
本文提出一个开源的软件在环（SIL）仿真平台，专为自主阿克曼车辆的研究与教学设计。该框架注重简洁性，便于小规模实验平台（如XTENTH-CAR）使用。系统采用开源工具构建，配备单目摄像头视觉系统，通过基于滑动窗口的车道检测方法，以极低计算开销捕捉环境刺激。平台支持灵活的算法测试与验证，研究人员可在易用的虚拟环境中实现并比较多种控制策略。为验证平台有效性，分别在SIL框架中实现了模型预测控制（MPC）与比例-积分-微分（PID）算法。结果表明，该平台能提供可靠的算法验证环境，适用于未来多智能体系统研究、教学应用以及低成本自动导引车（AGV）开发。代码已公开于 https://github.com/shantanu404/monosim.git。
原文摘要 · Abstract (English)
This paper presents an open-source Software-in-the-Loop (SIL) simulation platform designed for autonomous Ackerman vehicle research and education. The proposed framework focuses on simplicity, while making it easy to work with small-scale experimental setups, such as the XTENTH-CAR platform. The system was designed using open source tools, creating an environment with a monocular camera vision system to capture stimuli from it with minimal computational overhead through a sliding window based lane detection method. The platform supports a flexible algorithm testing and validation environment, allowing researchers to implement and compare various control strategies within an easy-to-use virtual environment. To validate the working of the platform, Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) algorithms were implemented within the SIL framework. The results confirm that the platform provides a reliable environment for algorithm verification, making it an ideal tool for future multi-agent system research, educational purposes, and low-cost AGV development. Our code is available at https://github.com/shantanu404/monosim.git.
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