Cash registers: EU experience/ Касова дисципліна: досвід Європи

Cash registers: EU experience/ Досвід Європи у впровадженні касових апаратів

Business

Cash registers: EU experience/ Касова дисципліна: досвід Європи

Automakers are now closer than ever to putting commercial self-driving cars on public roads. Governments, universities, and private companies have been making concerted efforts to develop a self-driving car since the 1970s, but the Grand Challenge presented by the Defense Advanced Research Projects Agency (DARPA) in 2004 was the first open competition created to encourage such research. The event and its follow-ups in 2005 and 2007 helped begin much of the modern development of autonomous vehicles.

These slides discuss the design changes that enable improvements in cost and performance to occur. The main types of design changes that lead to improvements are: 1) reductions in scale (e.g., transistors and Moore's Law); 2) creation of new materials; 3) increases in scale (e.g., internal combustion engines, oil tankers, production equipment). Some technologies experience these improvements directly and some indirectly through the impact of components on higher-level systems.

"Sensing Technologies for the Autonomous Vehicle," a Presentation from NXP Se...

For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/nxp/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit For more information about embedded vision, please visit: http://www.embedded-vision.com Tom Wilson, ADAS Product Line Manager at NXP Semiconductors, presents the "Sensing Technologies for the Autonomous Vehicle" tutorial at the May 2016 Embedded Vision Summit. Autonomous vehicles will necessarily utilize a range of sensing technologies to see and react to their surroundings. We are witnessing dramatic advances not just for embedded vision, but also in complementary technologies like radar and LiDAR. Each of these sensing technologies provides unique capabilities for giving a vehicle a complete view of its surroundings. This presentation compares vision-based sensing with complementary sensing technologies, explores key trends in sensors for autonomous vehicles, and analyses challenges and opportunities in fusing the output of multiple sensor technologies to enable robust perception and mapping for autonomous vehicles.

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For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-nasa-keynote For more information about embedded vision, please visit: http://www.embedded-vision.com Larry Matthies, senior research scientist at the NASA Jet Propulsion Laboratory, presents the "Using Vision to Enable Autonomous Land, Sea and Air Vehicles" keynote at the May 2016 Embedded Vision Summit. Say you’re an autonomous rover and you’ve just landed on Mars. Vexing questions now confront you: “Where am I and how am I moving?” “What obstacles are around me?” “Are the obstacles moving?” “What other objects are around me that matter to my mission?” As it turns out, Earth isn’t that different from Mars in this regard. If you’re an autonomous car or drone, you face similar challenges. You’ve got to find combinations of sensors that work across different illumination, weather, temperature, and vehicle dynamics; processors that fit the size, weight, and power constraints of the system; and algorithms that can answer the questions given the sensors and processors available. In this talk, Matthies gives an overview of autonomous vehicle computer vision applications, explores successful approaches, and illustrates concepts with application examples from applications on Earth and in planetary exploration.

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These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of autonomous vehicles are improving rapidly. LIDAR, other sensors, ICs, and wireless are experiencing rapid improvements that are enabling the overall cost of AVs to fall. For example, the latency of wireless systems is improving rapidly thus enabling vehicles to be controlled with wireless systems. This is also creating many new opportunities in the vehicle industry in the Internet of Things, data analytics, and logistics. The slides include a detailed discussion of AVs in Singapore, a likely early adopter.

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