Parallel Domain is a synthetic data platform that enhances perception performance for autonomy and robotics companies. Today they announced Data Lab, a new API to generate high-fidelity synthetic data for training and testing of perception systems. The solution enables ML engineers to create synthetic datasets with just a few lines of code, giving them control over dynamic virtual worlds to simulate any scenario imaginable.
Outdoor autonomous systems like autonomous vehicles, drones, and robotics are rapidly growing with the market expected to be valued at $5.68 billion by 2033 for emerging technologies such as the demand for high-quality, varied synthetic data. In order to maximize AV safety and adhere to regulations they must be trained on diverse, high-quality data sets to recognize and respond appropriately to a multitude of real-world situations. Data Lab alleviates the challenges of costly, time-consuming, and privacy-challenging data management endeavors through its synthetic offering. The platform allows the creation of varied, specific, and tough scenarios in a safe and controlled environment to ensure that autonomous systems are thoroughly tested and prepared, thereby enhancing AV safety and reliability.
Also Read: Must-Have CRM Integrations
“Autonomous systems, from ADAS to delivery drones, are already making our world safer and more accessible. But they’re only operating in limited contexts today,” said Kevin McNamara, CEO and Founder of Parallel Domain. “The launch of Data Lab moves us towards a world where ML developers across autonomy industries can generate exactly the datasets they need to prepare their systems for a wide variety of situations from someone crossing the street with a stroller to a tree falling in the middle of the road. Our customers have an increasing need for data to expand their operations. We’re proud to meet this need with Data Lab and contribute to a future where our lives are improved by safe autonomous systems every single day.”
Data Lab enables a high degree of control for customers by using 3D simulation to accurately reconstruct scenes and scenarios. Users can then tap into the Reactor module to describe any type of object they need, leveraging advanced generative AI technology to translate these situations into accurate, high-fidelity synthetic data.
The solution is a significant evolution in Parallel Domain’s suite of offerings, empowering customers with the ability to fully design, experiment with, and generate synthetic datasets on their own using the provided Python interface. Data Lab’s launch will catalyze significant growth for Parallel Domain by opening new avenues of opportunities including expansion of user base, services, and new industries and continuing the use of generative AI to drive content generation scalability