A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers. In this article, we focus on the adaptive cruise control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver's preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce a method to combine machine-learning algorithms with demographic information and expert advice into existing automated assistive systems.
Governor Andrew Cuomo of the State of New York declared last month that New York City will join 13 other states in testing self-driving cars: "Autonomous vehicles have the potential to save time and save lives, and we are proud to be working with GM and Cruise on the future of this exciting new technology." For General Motors, this represents a major milestone in the development of its Cruise software, since the the knowledge gained on Manhattan's busy streets will be invaluable in accelerating its deep learning technology. In the spirit of one-upmanship, Waymo went one step further by declaring this week that it will be the first car company in the world to ferry passengers completely autonomously (without human engineers safeguarding the wheel). As unmanned systems are speeding ahead toward consumer adoption, one challenge that Cruise, Waymo and others may counter within the busy canyons of urban centers is the loss of Global Positioning System (GPS) satellite data. Robots require a complex suite of coordinating data systems that bounce between orbiting satellites to provide positioning and communication links to accurately navigate our world.
After announcing plans this month to supply self-driving vehicles for Lyft's ride-hailing network, the autonomous tech developer has scored financial backing from Southeast Asian rideshare powerhouse Grab and plans to expand into Singapore. Singapore office will study that market as a potential place to deploy vehicles equipped with its software and self-driving hardware kits in government and business fleets, Tandon said. Amid the rush by auto and tech firms to perfect robotic vehicles, Tandon and his co-founders, who were all researchers from Stanford University's Artificial Intelligence Lab, founded Drive.ai to specialize in deep learning-based driving software for business, government and shared vehicle fleets. Small relative to well-funded programs at Waymo, General Motors' Cruise, Uber's Advanced Technology Vehicle Group and Ford's Argo AI, Mountain View, California-based Drive.ai has made quick progress.
Jaguar Land Rover, taking a page from the European luxury car playbook, is offering increasingly attractive performance versions of its entry-level sports cars. Quicker, faster and better-handling than the base F-Type, the SVR model is a high-octane sports car disguised as a luxury car. The SVR versions of Jaguar Land Rover vehicles represent a still smaller slice of the pie. The F-Type SVR's size, limited storage and seating configuration will disqualify it for a lot of buyers.
They were mechanical marvels of technology that could perform many impressive functions within and unto themselves, but artificial intelligence (AI), machine learning, true driver personalization, and external data exchange capabilities were still conceptual. Its value will be judged by how elegantly it understands and communicates with its users using speech and natural language, while accessing and delivering a world of information from a wide range of "expert" sources to instantly and/or proactively deliver the right answer, content, or action. Similarly, the automotive assistant, while highly capable itself, delivers the best experience for users by intelligently coordinating all pieces of the connected world ecosystem. Taken together, rapid advances in AI interoperability, personalization, and contextualization will allow automotive assistants to significantly enhance car mobility for drivers and passengers.
Nicola Mortimer, head of business products, marketing and operations at Three Ireland, on how machine learning can drive efficiency rather than drive people out of their jobs. Machine learning is predicted to be an integral part of more than 300m new smartphones sold this year. So, should we be excited or fearful for our jobs? It has been predicted that machine learning capabilities will be present in more than 20pc of smartphones sold globally in 2017. With few devices more ubiquitous in the developed world than the smartphone, machines that learn will now be at the fingertips of a large percentage of the population.
Many of the top players in technology and automobiles are fervently working towards a world in which autonomous driving vehicles are commonplace. Some current vehicles, like the Tesla Model S, already offer self-driving, auto-pilot capabilities, but these are a precursor to fully autonomous vehicles that operate with virtually no human intervention. As you'd probably expect, Intel is actively working in the area as well. Massive amounts of processing power and storage are needed to churn through and store the deep learning models that will disseminate data for autonomous vehicles. Today the company posted a short video that gives a glimpse into its Autonomous Driving Lab in Chandler, Arizona.
This year artificial intelligence (AI) with the associated technologies such as smart IoT sensors and increasingly powerful and seamless human machine interfaces (HMIs) proved to be the cynosure of all eyes that passed by. Most global automobile companies are working on driverless cars that are based on continuous advances in computer vision and deep learning technologies. This is another example where smart sensors and human machine interactions, when combined with artificial intelligence technologies could create tangible advances in the way we drive, work and play. For instance, Cisco is working with Hyundai to create a strong network backbone for their vehicles that would help Hyundai to simplify its network and seamless connect to other vehicles, through the cloud.
Uber has taken another definitive step toward eliminating human drivers from its fleet of vehicles. This week, the ride service pioneer created a research division, known as AI Labs, while simultaneously acquiring machine learning startup Geometric Intelligence for an undisclosed sum. The new lab will become the incubator for products and technology that Uber can use to further automate the operation of its vehicles and service. The new lab will be initially populated by the 15 employees that made up Geometric Intelligence, a New York-based startup that assembled a team of young AI university researchers and enthusiasts. Heading the new Uber lab will be Geometric Intelligence founding CEO Gary Markus, a professor at NYU.
In pattern recognition, the K-Nearest Neighbor algorithm (KNN) is a method for classifying objects based on the closest training examples in the feature space. KNN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. The KNN algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k 1, then the object is simply assigned to the class of its nearest neighbor [Source: Wikipedia]. In today's post, we explore the application of KNN to an automobile manufacturer that has developed prototypes for two new vehicles, a car and a truck.