44 For example, thermomechanical fatigue predictions have been upgraded to process approximately 20,000 design elements in only 24 hours, yielding a 20% improvement in the life of components and saving over 15,000 hours of computational time. Reduced Order Modeling software for high-fidelity simulation and test data trains and validates AI/ML models. By training AI/ML models on comprehensive datasets, this technology enables engineers to gain robust, reliable and trustworthy insights, helping to eliminate the common issue of AI drift. This can accelerate simulation models to the point where a detailed fuel-cell plant model runs faster than real time with the same accuracy as a full system model. This enables activities such as model-in-the-loop controller development and testing to be done faster, shortening the overall development cycle by around 25%. A highly accurate digital model of an expansive network of roads in Los Angeles, California is supporting the development of automated driving technologies. The virtual environment enables engineers to conduct extensive simulation testing of AVs before progressing to the public road. The Los Angeles model features a 36 km loop, making it one of the largest public road models developed to date. It is navigable in both directions and offers engineers an extensive real world-based environment for conducting comprehensive testing. Capturing the intricate layout of the city’s roads, the model includes diverse configurations such as highways, split dual-carriageways and single-carriageway sections. The digital model has been created using survey-grade Lidar scan data to create a vehicle dynamics-grade road surface, which is accurate to within 1 mm in height across the entire 36 km route. The digital twin features 12,400 buildings, 40,000 pieces of vegetation and more than 13,600 other pieces of street furniture, including street lights, traffic lights, road signs, road markings, walls and fences. All of these objects have been physically modelled with appropriate material characteristics, which is critical for testing sensor systems. The model provides OEMs with the ability to thoroughly train and test perception systems in a safe and repeatable environment before correlating these simulated results on the public road for use by vehicles in real-world scenarios. This provides a better understanding of what happened in that particular scenario, and allows new perception systems and control algorithms to be tested exhaustively in the same situation before deploying them on a vehicle. It also incorporates aspects that challenge automated driving technologies, such as roadside parking, islands separating carriageways, drop kerbs in residential areas, rail crossings, bridges, tunnels and a large number of junctions with varying complexity. GenAI in the cloud is also being used for scenario generation alongside HiL systems. A large language model (LLM) and retrieval-augmented generation (RAG) architecture allows developers to create a vast number of complex October/November 2024 | Uncrewed Systems Technology Combining simulation and hardware in the loop testing (Image courtesy of dSpace) A Lidar test system (Image courtesy of Keysight Technologies) The digital model [uses] survey-grade Lidar scan data to create a vehicle dynamics-grade road surface, accurate to within 1 mm in height
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