By addressing issues concerning data standardization and training complexities, the market can deliver innovative solutions to automakers and fleet operators.
The automotive industry is currently experiencing a transformation driven by electrification, connectivity, and autonomous driving. Vehicles require extensive computational capabilities and generate massive amounts of data. Future cars will embrace new architectures, becoming software-defined “computers on wheels” capable of hosting advanced applications, machine learning algorithms, and continuous reconfiguration. Ongoing advancements in software and hardware, as well as their interface, are pushing the boundaries of performance and functionality. However, the same technology that will bring this vision to life also introduces new challenges.
TTTech Auto and proteanTecs have introduced a new approach, combining technologies to allow for predictive maintenance and failure prevention strategies of advanced automotive electronic systems. By integrating TTTech Auto’s MotionWise software platform, designed for advanced driver assistance systems (ADAS) and autonomous driving applications, with proteanTecs deep data analytics we jointly developed a unique solution for deploying and fully utilizing cutting-edge SoCs in mission-critical systems, enabling highest performance ECUs that meet the most stringent safety demands.
The need for deep data
While it is agreed that advanced health management and prognosis applications for electronics are crucial, the market confronts obstacles related to data integration, model accuracy, and the management of diverse vehicle types. These challenges, in turn, create openings for IoT-enabled sensors, machine learning algorithms, and advanced analytics. By addressing issues concerning data standardization and training complexities, the market can deliver innovative solutions to automakers and fleet operators.
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