Parallel Domain, a leader in simulation and autonomous system testing, proudly announces it is now a member of the Mcity public-private mobility partnership based at the University of Michigan. Mcity partners from industry, government and academia are forward-thinking technology leaders shaping the future of autonomous mobility.

Simulation can become transformative, with the promise of testing unlimited scenarios safely in a virtual world. However, previous approaches have struggled to achieve sufficient realism and scale to be trusted by teams to translate to real-world performance. But what if we could recreate the real world as a series of digital twins that give us the knobs and controls to test all of these permutations? It would almost be like driving one mile to generate a thousand miles of useful tests.

Models trained on PD Replica environments showed a 31.4% improvement over those trained on procedural data in mean average precision (mAP)

Parallel Domain customers experience impressive double-digit performance gains in sign perception tasks.

Parallel Domain (PD) synthetic data improves performance on optical flow tasks by 18.5% by matching flow magnitudes.

Synthetic data best practices we learned from working with many perception teams and our internal research.

Parallel Domain data improves unsupervised domain adaptation performance by 30% vs. GTA with no changes to model architecture.

The company reimagining how AI learns, realizing an autonomous future for everyone and everything

Parallel Domain synthetic data significantly improves performance on rare classes such as bicycles with no changes to model architecture.