"Most current advanced driver assistance systems based on radar and cameras are not capable of accurately detecting and classifying objects – such as cars, pedestrians or bicycles – at a level required for autonomous driving," said Sachin Lawande, president and CEO of Visteon, a leading global cockpit electronics supplier. "We need to achieve virtually 100 percent accuracy for autonomous driving, which will require innovative solutions based on deep machine learning technology. Our Silicon Valley team, with its focus on machine learning software development, will be a critical part of our autonomous driving technology initiative." Visteon's recently opened facility in the heart of Silicon Valley will house a team of engineers specializing in artificial intelligence and machine learning. The center is located close to the West Coast offices of various automakers and tech companies, as well as Stanford University and the University of California, Berkeley – two of the leading universities for artificial intelligence and deep learning in the U.S. In addition to leading Visteon's artificial intelligence efforts, the Silicon Valley office will play a key role in delivering control systems, localization and vision processing – interpreting live camera data and converting it to information required for autonomous driving.
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 achieve 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 the rapid progress in DNN technology, the two companies plan to make the technology flexibly extendable to various network configurations.
Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, Dirichlet process mixture models, and Pitman-Yor process mixture models make this assumption, as do all other infinitely exchangeable clustering models. However, for some applications, this assumption is inappropriate. For example, when performing entity resolution, the size of each cluster should be unrelated to the size of the data set, and each cluster should contain a negligible fraction of the total number of data points. These applications require models that yield clusters whose sizes grow sublinearly with the size of the data set. We address this requirement by defining the microclustering property and introducing a new class of models that can exhibit this property. We compare models within this class to two commonly used clustering models using four entity-resolution data sets.
TOKYO-- Toyota Motor Corp. TM -0.84 % 's biggest parts supplier, Denso Corp. DNZOY 1.33 %, said Friday it planned to buy a majority stake in a maker of self-driving technology as it seeks to beef up its offerings to compete with global rivals. The deal to take a 51% stake in Fujitsu Ten Ltd., which builds the sort of radar systems used in autonomous driving systems, is Denso's third such deal since December. Denso will acquire shares from parent company IT firm Fujitsu Ltd. FJTSY 0.04 % The companies declined to say how much Denso would pay. Separately, Toyota will retain its current stake in Fujitsu Ten of a little over a third. Car makers from General Motors Co. GM -0.77 % to Tesla Motors Inc. are adding systems that take over some driving tasks in order to make cars safer.
Two additional deaths in Malaysia were linked to ruptured air bag inflators made by Takata Corp., further damaging the reputation of the Japanese supplier as it works to comply with a U.S. order to expand a record recall. Two fatal Honda car crashes in Malaysia, one on April 16 and the other just last Monday, involved ruptured driver-side air bag inflators made by Takata, according to a statement by Honda Motor Co. The air bags had not been replaced though the two vehicles were included in recalls announced by the authorities, the automaker said. The U.S. National Highway Traffic Safety Administration (NHTSA) on Wednesday ordered Takata to replace as many as 40 million additional air bags in the U.S., more than doubling what has been announced. At least 13 deaths are now linked to the malfunctioning devices, underscoring the scale of the crisis confronting President Shigehisa Takada, who has seen his family company's market value plunge by 75 percent over the past year.