Leveraging advanced, physics-based simulation and innovative sensor data processing technologies, the new Siemens solution is designed to help automakers and their suppliers address this industry challenge with the potential to shave years off the development, verification and validation of self-driving cars. TASS' PreScan simulation environment produces highly realistic, physics-based simulated raw sensor data for an unlimited number of potential driving scenarios, traffic situations and other parameters. The data from PreScan's simulated LiDAR, radar and camera sensors is then fed into Mentor's DRS360 platform, where it is fused in real time to create a high-resolution model of the vehicle's environment and driving conditions. Customers can then leverage the DRS360 platform's superior perception resolution and high-performance processing to test and refine proprietary algorithms for critical tasks such as object recognition, driving policy and more. "Automakers are quickly realizing that physical prototypes and road testing alone cannot reproduce the multitude of complex driving scenarios self-driving cars will encounter.
United Technologies launched a digital accelerator in Brooklyn a year ago with a $300 million investment aimed at developing software that spans the company's various units such as Otis, Pratt & Whitney and Carrier to name a few. Vince Campisi, chief information officer at UTC, is overseeing UTC's digital efforts with the aim of connecting software, analytics and the Internet of things to drive the company's products and services and improve customer operations overall. We caught up with Campisi to talk shop and digitization. Here are a few highlights of our chat. Campisi noted that classically IT groups focused on operations, but the move to digitization is bringing more groups into the mix.
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You may think of German industrial powerhouse Siemens as being primarily a machine builder, but the company has a range of digital offerings that span throughout the entire value chain in manufacturing. From product development, production engineering, and production execution, the company offers a consistent data model across all levels of manufacturing, thanks to its product lifecycle management, digital twin software, and MindSphere IoT platform. Content Director Brian Buntz wrote recently about the resources Siemens is throwing at software, and while that's significant, I'm more interested in Siemens' AI and machine learning work. Michael May, Ph.D., the company's head of technology field business analytics and monitoring, told me at Hannover Messe that the corporation has been working on AI projects for decades. For instance, more than 20 years ago, Siemens implemented neural networks in more than 30 steel plants to monitor and improve quality, process, and efficiencies.