42 Focus | Simulation and testing handling different lens parameters for cameras, and rotating or stationary flash Lidar. Here, it’s the complexity that is the challenge. Four cameras with suppliers’ models is not a problem, but combining the data from 12 cameras, five radar and one rotating lidar with a map of the environment is where the challenges enter. One way to tackle this is to use a multi-threaded approach to distribute the computation for all the sensors over multiple GPUs, which is not trivial for simulator developers. While 20 sensors on one GPU would be very slow, five to eight will provide real-time performance. Vital APIs The APIs for the models are vital and increasingly have to take distributed GPUs into account. The application-programming interfaces address the critical need for high-fidelity sensor simulations to safely explore the various real-world scenarios that autonomous systems will encounter. CARLA, for example, is an open-source AV simulator that is used by more than 100,000 developers. APIs allow users to add high-fidelity sensor simulation from the cloud into existing workflows. It has been developed from the ground up to support the development, training and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites, environmental conditions, full control of all static and dynamic actors, map generation and more. The API allows users to control all aspects related to the simulation, including traffic generation, pedestrian behaviour, weather and sensors. One platform uses proven coveragedriven methodologies for the verification of driverless cars. The tool generates the myriad tests required to cover all relevant scenarios that may be encountered in the real world. Scenarios are defined using ASAM OpenSCENARIO 2.0, a human-readable, scenariodescription language that has been recently adopted as an industry standard. Adding genAI AI-based Simulation Predictor and Reduced Order Modeling software boost the development of digital twins. The AI Simulation Predictor uses ML with built-in accuracy awareness to help finetune and optimise digital twins by tapping into historical simulation studies and accumulated knowledge. This comes as Siemens teams with Amazon to provide the PAVE360 tool in the latter’s cloud for building digital twins of cars using ARM CPU models. One of the most significant challenges in AI-powered simulation is AI drift, where models extrapolate inaccurately when faced with uncharted design spaces. To address this, the Simulation Predictor introduces accuracy-aware AI that self-verifies predictions, helping engineers to conduct simulations that are not only accurate but also reliable in the context of real-world industrial engineering. October/November 2024 | Uncrewed Systems Technology The design flow for simulation and validation (Image courtesy of Foretellix) Combining validation tools with the Nvidia Omniverse digital-twin tool (Image courtesy of Foretellix) Accuracy-aware AI self-verifies predictions, helping engineers to conduct simulations that are not only accurate but also reliable
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