Blog
The Power of “Shift Left”
"Shift Left" is a transformative paradigm that reorients the entire testing and validation process. Instead of waiting until the later stages of development to test on real-world tracks and roads, it advocates for integrating testing through advanced simulation, from the very beginning and continuously through the development cycle. Think of it like moving quality control from the end of the assembly line to every single step of the manufacturing process.
New Mcity Digital Twin by Parallel Domain Brings Physical AI Research Closer to Reality
Parallel Domain today announced the completion of a real-world fidelity Replica of the Mcity Test Facility at the University of Michigan. This real-fidelity simulation environment allows Mcity members and researchers to run perception, planning, prediction, and control tests with realism nearly indistinguishable from the physical track.
Protecting our Most Vulnerable: Can Simulation Reliably Test Pedestrian Detection Models?
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.
Parallel Domain and NVIDIA Cosmos Transfer: Additional Scene Variation for High-Fidelity Sensor Simulation
PD Replica employs advanced scene reconstruction and rendering techniques to provide developers with realistic simulation environments suitable for open and closed-loop testing and validation of perception models. However, while PD Replica Sim can programmatically generate scene variations, there are limitations to the full breadth of environmental controls available today, and where Cosmos Transfer with an open-ended prompt can enhance the quantity of variations available.
Diagnose Model Failure Points with PD Replica Sim
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.
Signs of Trust: Matching Real-World Performance with PD Replica Sim
Determine whether the performance observed on the PD Replica Sim dataset would closely mirror those obtained from the real-world dataset.
NVIDIA Cosmos vs PD Replica Sim – Which is right for you?
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.
Parallel Domain joins Mcity to power simulations on PD Replica digital twin
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.
Testing for the Future: Advance Perception Via Simulation
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.
Closing the sim-to-real gap with PD Replica
Models trained on PD Replica environments showed a 31.4% improvement over those trained on procedural data in mean average precision (mAP)