DENSO Corp. and Toshiba Corp. have reached an agreement to jointly develop an artificial intelligence technology called Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems that have been independently developed by the two companies to help achieve advanced driver assistance and automated driving technologies.
DNN, an algorithm modeled after the neural networks of the human brain, is expected to perform recognition processing as accurately as, or even better than the human brain. To develop automated driving, automotive computers need to be able to identify different road traffic situations, including a variety of obstacles and road markings, availability of road space for driving and potentially dangerous situations. In image recognition based on conventional pattern recognition and machine learning, objects that need to be recognized by computers must be characterized and extracted in advance.
In DNN-based image recognition, computers can extract and learn the characteristics of objects on their own, thus significantly improving the accuracy of detection and identification of a wide range of objects.
Because of rapid advancements in DNN technology, the two companies plan to make the technology flexibly extendable to various network configurations. They also will make the technology able to be implemented on in-vehicle processors that are smaller, consume less power and feature other optimizations.
DENSO has been developing DNN-IP for in-vehicle applications. By accelerating the process to commercialize DNN-IP through the joint development and incorporating DNN-IP in in-vehicle cameras, DENSO will develop high-performance, advanced driver assistance and automated driving systems, and continue to contribute to building a safe and secure automotive society for people around the world, not just for drivers and pedestrians.
In addition to its conventional image processing technologies, Toshiba will partition this jointly developed DNN-IP technology into dedicated hardware components and implement them on its in-vehicle image recognition processors to improve their image processing performance and enable them to process images using less power than image processing systems with digital signal processors or graphics processing units.
