SUMO is a microscopic traffic simulation suite designed to model the movement of individual vehicles and pedestrians across large-scale urban road networks. It functions as a multimodal transport simulator that integrates cars, pedestrians, bicycles, railways, and waterways within a single environment, supported by tools for road network generation and traffic demand modeling. The project distinguishes itself through specialized toolsets for environmental impact analysis, which calculate vehicle emissions and energy consumption for electric and hybrid fleets. It provides comprehensive capabil
A/B Street is an open-source traffic simulation and urban planning tool that models how cars, bikes, and pedestrians move through real-world street networks. It imports data from OpenStreetMap to build detailed, lane-level road models, then runs discrete-event simulations to analyze travel times, delays, and congestion patterns across different infrastructure scenarios. The project provides an interactive map editor for modifying road geometry, lane configurations, traffic signals, and access restrictions, with full undo/redo support. Users can design low-traffic neighborhoods by placing moda
CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving software. It functions as an urban traffic simulator that generates realistic vehicle and pedestrian behavior and as a synthetic sensor data generator producing LiDAR, Radar, and camera data. The platform distinguishes itself through its deep integration with robotics frameworks, specifically providing native connectivity to ROS2 nodes for robotic control and data processing. It supports the training of driving models via imitation and reinforcement learning within a controlle
This research framework provides a deep learning driving simulator and a multimodal data pipeline for autonomous vehicle research. It centers on the creation of synchronized autonomous vehicle datasets, which combine high-frequency vehicle telemetry with camera frames to train neural networks. The project implements a convolutional neural network trainer specifically designed to predict steering angles and vehicle transition states from visual data. It features generative capabilities, using autoencoders and transition models to synthesize driving environments and simulate future vehicle move
SUMO is a microscopic traffic simulator and road network generator designed to model the detailed movement of individual vehicles, pedestrians, and public transport. It serves as a multimodal transport simulator and an autonomous vehicle simulator, enabling the analysis of interactions between different transport modes and the testing of automated driving behaviors.
Les fonctionnalités principales de eclipse-sumo/sumo sont : Autonomous Vehicle Simulators, Transport Simulations, Urban Traffic Simulation, Road Network Importers, Socket-Based Control Planes, Microscopic Traffic Simulation, Road Network Generators, Multi-Modal Network Models.
Les alternatives open-source à eclipse-sumo/sumo incluent : eclipse/sumo — SUMO is a microscopic traffic simulation suite designed to model the movement of individual vehicles and pedestrians… a-b-street/abstreet — A/B Street is an open-source traffic simulation and urban planning tool that models how cars, bikes, and pedestrians… carla-simulator/carla — CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving… commaai/research — This research framework provides a deep learning driving simulator and a multimodal data pipeline for autonomous… nvidia/isaac-gr00t. udacity/self-driving-car-sim — This project is an autonomous vehicle simulator designed to validate self-driving logic and train deep learning…