The F1Tenth Two-Agent Simulator is a specialized simulation framework built on ROS and Gazebo to emulate two 1/10th scale autonomous vehicles racing or cooperating on custom tracks. It supports realistic tire-model physics, sensor emulation, collision detection, and data logging. Users can plug in their own planning and control algorithms, adjust agent parameters, and run head-to-head scenarios to evaluate performance, safety, and coordination strategies under controlled conditions.

Artificient provides a cutting-edge computer-vision-powered smart driving application designed to enhance vehicle collision detection and improve overall safety on the road. Utilizing proprietary machine learning and advanced signal processing techniques, the app analyzes user smartphone data to accurately determine the severity and direction of accidents. This technology aims to empower users with precise mobility insights, fostering safer and more sustainable driving practices.

This open-source framework combines the AgentSimJs agent modeling library with Three.js's 3D graphics engine to deliver interactive, browser-based multi-agent simulations. Users can define agent types, behaviors, and environmental rules, configure collision detection and event handling, and visualize simulations in real time with customizable rendering options. The library supports dynamic controls, scene management, and performance tuning, making it ideal for research, education, and prototyping of complex agent-based scenarios.

AgentSimulation is an open-source Python library built on Pygame for simulating multiple autonomous agents in a 2D environment. It allows users to configure agent properties, steering behaviors (seek, flee, wander), collision detection, pathfinding, and interactive rules. With real-time rendering and modular design, it supports rapid prototyping, teaching simulations, and small-scale research in swarm intelligence or multi-agent interactions.

Simple Playgrounds provides a modular platform for building interactive 2D grid environments where agents can navigate mazes, interact with objects, and complete tasks. Users define environment layouts, object behaviors, and reward functions via simple YAML or Python scripts. The integrated Pygame renderer delivers real-time visualization, while a step-based API ensures seamless integration with reinforcement learning libraries like Stable Baselines3. With support for multi-agent setups, collision detection, and customizable physics parameters, Simple Playgrounds streamlines the prototyping, benchmarking, and educational demonstration of AI algorithms.