Parallel Domain

Simulation API to test autonomous systems

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Description

Parallel Domain provides a sophisticated platform featuring an API, SDK, and Web Tools designed for machine learning, computer vision, and perception teams. The core offering is high-fidelity, software-in-the-loop sensor simulation, grounded in real-world scans, which supports streaming of camera, lidar, and radar data complete with full annotations. This enables teams to programmatically test and analyze the performance of perception systems with precision and scalability, supporting both open and closed-loop simulation methodologies.

Addressing the inherent challenges of real-world testing—which is often difficult, risky, and expensive—Parallel Domain helps bridge the sim-to-real gap. Its PD Replica feature allows for the generation of digital twins at scale from user capture data, creating fully annotated, simulation-ready environments with significant realism and variety. This capability ensures that simulation performance closely matches real-world outcomes, facilitating the development of robust AI for autonomous systems by enabling the creation of diverse synthetic datasets, including critical edge cases.

Key Features

Simulation API: Programmatically test and analyze perception system performance at scale.
SDK and Web Tools: Comprehensive toolkit for machine learning, computer vision, and perception teams.
High-Fidelity Sensor Simulation: Delivers realistic camera, lidar, and radar data grounded in real-world scans.
Full Data Annotations: Provides detailed and accurate annotations with all simulated sensor data.
Open & Closed-Loop Simulation: Offers flexible simulation modes to accommodate diverse testing requirements.
PD Replica: Generates scalable, simulation-ready digital twins from real-world capture data, ensuring realism and variety.
Synthetic Data Generation: Creates diverse datasets, including edge cases, to improve machine learning model performance.

Use Cases

Testing and validating perception systems for autonomous vehicles.
Training machine learning models for object detection, such as emergency vehicles and traffic lights.
Simulating diverse and challenging scenarios for robust AI development.
Generating synthetic datasets to augment real-world data and cover edge cases effectively.
Developing and testing robot autonomy systems across industries like automotive, aerial, and robotics.
Accelerating machine learning development for applications in agriculture, warehouse logistics, and security.