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MonoSIM: An open source SIL framework for Ackermann Vehicular Systems with Monocular Vision

Shantanu Rahman, Nayeb Hasin, Mainul Islam, Md. Zubair Alom Rony, Golam Sarowar

Abstract

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.

MonoSIM: An open source SIL framework for Ackermann Vehicular Systems with Monocular Vision

Abstract

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.
Paper Structure (9 sections, 14 equations, 16 figures, 1 table)

This paper contains 9 sections, 14 equations, 16 figures, 1 table.

Figures (16)

  • Figure 1: Original input frame
  • Figure 2: Birds Eye View
  • Figure 3: Detected and refined chessboard corners overlaid on the grayscale image.
  • Figure 4: Lane visible in camera
  • Figure 5: Bird’s Eye view
  • ...and 11 more figures