"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.
Not so long ago, the consumer electronics show (CES) was the place to exhibit the latest in computers, mobile devices and other electronic gadgets. It still is although the accent is changing. Every year, this Las Vegas tech fest attracts a rising tide of automotive suppliers taking space both inside and outside the convention centre halls. This month's management briefing turns the spotlight on some of those exhibitors operating in the brave new auto world of voice control, artificial intelligence (AI) and machine learning. Among those unveiling autonomous driving concepts at CES included Chrysler, Toyota, Honda and Faraday Future.
Samsung Electronics Co. is making a drive for control of the car. The South Korean smartphone maker said Monday that it would buy U.S. auto-parts supplier Harman International Industries Inc., based in Stamford, Conn., for $8 billion in an all-cash deal that instantly makes Samsung a major player in the world of automotive technology. The deal -- Samsung's biggest acquisition in its history -- reshapes the pecking order in the global automotive supply chain, reflecting a quickening pace of innovation and an increased role for companies with deep pockets and a keen understanding of mobile services. Harman, an audio pioneer that dates back to 1953, has in recent years pushed aggressively into the automotive world under CEO Dinesh Paliwal, and has secured billions in new business, including big contracts with General Motors Co. and Fiat Chrysler Automobiles NV. It has projected an order backlog of $24 billion, more than three times annual revenue, and about two-thirds of its current sales come from auto makers.
GMGear is an equation and knowledge based system that uses an iterative process to generate multiple possible solutions for a gear set design. It evaluates and recommends the best gear set to meet performance requirements, geometric constraints and manufacturing considerations. Performance requirements include input torque and speed, gear ratio, weight, noise and duty cycle life. Geometry constraints are the envelope into which the gear set must fit, as defined by axial length and vertical size. Manufacturing considerations include tolerances, types of materials and the use of existing cutters, carriers and mating gears. GMGear takes advantage of Object-Oriented technology for implementation of domain information, and uses a sophisticated back chaining mechanism to apply equations towards reaching design solutions. We represented all gear variables and equations, as well as all physical components, methodology components and design results as objects. We designed and implemented this system so that the domain experts are responsible for maintaining the domain knowledge.