SelfDrivingCarSimulator

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SelfDrivingCarSimulator provides an open-source Python Pygame–based platform enabling users to simulate, train, and evaluate reinforcement-learning–driven self-driving car agents on configurable road maps with adjustable sensors and performance logging.
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May 15 2025
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SelfDrivingCarSimulator

SelfDrivingCarSimulator

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SelfDrivingCarSimulator
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What is SelfDrivingCarSimulator?

SelfDrivingCarSimulator is a lightweight Python framework built on Pygame that offers a 2D driving environment for training autonomous vehicle agents using reinforcement learning. It supports customizable track layouts, configurable sensor models (like LiDAR and camera emulation), real-time visualization, and data logging for performance analysis. Developers can integrate their RL algorithms, adjust physics parameters, and monitor metrics such as speed, collision rate, and reward functions to iterate quickly on self-driving research and educational projects.

Who will use SelfDrivingCarSimulator?

  • Reinforcement learning researchers
  • University students in AI and robotics
  • Hobbyists exploring autonomous driving
  • Educators teaching machine learning

How to use the SelfDrivingCarSimulator?

  • Step1: Clone the repository from GitHub: git clone https://github.com/mmehdi292/SelfDrivingCarSimulator.git
  • Step2: Navigate into the project folder and install dependencies: pip install -r requirements.txt
  • Step3: Configure track layouts and sensor parameters in config files
  • Step4: Run the simulator: python simulator.py
  • Step5: Integrate your RL agent code and start training
  • Step6: Monitor performance metrics via the built-in dashboard

Platform

  • mac
  • windows
  • linux

SelfDrivingCarSimulator's Core Features & Benefits

The Core Features

  • 2D self-driving car simulation via Pygame
  • Customizable track editor
  • Configurable sensor models (camera, LiDAR)
  • Reinforcement learning algorithm integration
  • Real-time visualization and metrics logging

The Benefits

  • Easy setup and lightweight
  • Fully open-source and extensible
  • Custom track and sensor configuration
  • Rapid prototyping for RL agents
  • Educational documentation and examples

SelfDrivingCarSimulator's Main Use Cases & Applications

  • Training reinforcement-learning agents for lane keeping and obstacle avoidance
  • Benchmarking self-driving algorithms in a controlled 2D environment
  • Educational demonstrations in AI and robotics courses
  • Prototype testing of sensor fusion and control strategies

FAQs of SelfDrivingCarSimulator

SelfDrivingCarSimulator Company Information

SelfDrivingCarSimulator Reviews

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Do You Recommend SelfDrivingCarSimulator? Leave a Comment Below!

SelfDrivingCarSimulator's Main Competitors and alternatives?

  • CARLA Simulator
  • Microsoft AirSim
  • Udacity Self-Driving Car Simulator
  • TORCS

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