Pedestrian protection is no longer optional. It’s becoming a regulatory requirement, and perception systems must be tested rigorously to ensure they can perform reliably in the real world. But how can teams meet this demand at scale? Real-world testing is expensive, slow, and often fails to capture rare but critical edge cases. Simulation provides a powerful alternative, but only if it can be trusted. In this blog post, we put PD Replica Sim to the test by benchmarking it against real-world data from the Waymo Open Dataset.

ML teams can use PD Replica Sim to push their autonomous system to its limits, interrogating model weaknesses and testing its generalization more rigorously than real-world data typically allows —covering tricky edge cases, handling occlusion and orientation sensitivity, and even evaluating model performance on unseen traffic sign types.

PD Replica Sim offers a level of control, fidelity, and determinism that NVIDIA Cosmos can’t match. In this post, we’ll break down what Cosmos is, where it shines, and where it falls short. Then we’ll compare it point-by-point with PD Replica Sim, showing you which platform is better suited for your specific needs.

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.

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