Driverless Car Technology
Driverless Car Technology

Author:

Publisher: LexInnova Technologies, LLC

Published: 2016-02-25

Total Pages: 24

ISBN-13:

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Driverless cars represent a disruptive technological change in transportation as we know it. These vehicles are capable of sensing, navigating, and communicating with their external surroundings without any human intervention. They leverage various technologies including imaging, radar, laser optics, and GPS to navigate through dynamically changing road environments. In this report, we analyze the Intellectual Property (Patents) landscape of driverless car technology. Our analysis reveals key aspects relating to innovation in this technology, including filing trends, top assignees, their portfolio strength, and geographical coverage.

Assessing the Status of Autonomous Vehicles Innovation Using Patent Data
Assessing the Status of Autonomous Vehicles Innovation Using Patent Data

Author: Mamy Traore

Publisher:

Published: 2020

Total Pages: 59

ISBN-13:

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"The transportation industry is undergoing an unprecedented revolution as researchers in the field expect the adoption of autonomous vehicles (AV) in a not-too-distant future. Even though there is no fully automated vehicle on the road currently, several features of driver’s assistance (e.g., lane departure warning, rear cameras, blind-spot warning) are integrated into most of the recent vehicles. It is therefore fundamental for industry leaders and policymakers to comprehend the state-of-the-art of AV innovation. The main purpose of this study is to assess the current status of AV innovations in the U.S. market. My analysis, based on more than 2,000 patents retrieved from the United States Patent and Trademark Office’s (USPTO) PatentsView database, has five main findings. First, there is a significant increase in autonomous vehicle patents approved by USPTO since 2010. Between 2010 to 2018, the number of patents increased by about 18 folds from 27 to 516. Secondly, in terms of AV innovators, the new entrant high-tech companies are taking over the incumbent automakers in the AV technologies. Third, industries involved in AV innovation have unequal levels of development in different technology sectors and fields. High-tech companies are leading in smart environment technologies. The incumbent automakers had an established predominance in the vehicle platform technologies. Fourth, of all the patents approved by the USPTO, about two-thirds are held by US companies, and one third held by foreign companies primarily from Asia and Europe. Fifth, in the US, California is the epicenter of AV innovation with nearly 40 percent of US patents. Michigan holds 18 percent of the total, given the presence of traditional automobile manufacturers including Ford and GM."--Abstract.

Measuring Innovation in the Autonomous Vehicle Technology
Measuring Innovation in the Autonomous Vehicle Technology

Author: Maryam Zehtabchi

Publisher: WIPO

Published: 2019-11-08

Total Pages: 36

ISBN-13:

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Automotive industry is going through a technological shock. Multiple intertwined technological advances (autonomous vehicle, connect vehicles and mobility-as-a-Service) are creating new rules for an industry that had not changed its way of doing business for almost a century. Key players from the tech and traditional automobile sectors – although with different incentives – are pooling resources to realize the goal of self-driving cars. AV innovation by auto and tech companies’ innovation is still largely home based, however, there is some shifting geography at the margin. AV and other related technologies are broadening the automotive innovation landscape, with several IT-focused hotspots – which traditionally were not at the center of automotive innovation – gaining prominence.

Roadmapping Future
Roadmapping Future

Author: Tuğrul U. Daim

Publisher: Springer Nature

Published: 2021-03-16

Total Pages: 765

ISBN-13: 3030505022

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This volume presents a portfolio of cases and applications on technology roadmapping (TRM) for products and services. It provides a brief overview on criteria or metrics used for evaluating the success level of TRM and then offers six case examples from sectors such as transportation, smart technologies and household electronics. A new innovation in this book is a section of detailed technology roadmap samples that technology managers can apply to emerging technologies.

Autonomous Driving
Autonomous Driving

Author: Markus Maurer

Publisher: Springer

Published: 2016-05-21

Total Pages: 698

ISBN-13: 3662488477

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This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".

Digital Transformations
Digital Transformations

Author: Daim, Tugrul U.

Publisher: Edward Elgar Publishing

Published: 2022-01-18

Total Pages: 192

ISBN-13: 1789908639

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Technology is not just limited to technology companies, it impacts sectors such as healthcare, agriculture, and security. In the last few decades, countries, too, have started developing technologies or integrating technologies into their systems. As a result, all countries, regardless of size, need to understand the management of engineering and technology concepts. Digital Transformations reviews fundamentals and applications through existing and emerging technologies all around the world.

Learning to Drive
Learning to Drive

Author: David Michael Stavens

Publisher: Stanford University

Published: 2011

Total Pages: 104

ISBN-13:

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Every year, 1.2 million people die in automobile accidents and up to 50 million are injured. Many of these deaths are due to driver error and other preventable causes. Autonomous or highly aware cars have the potential to positively impact tens of millions of people. Building an autonomous car is not easy. Although the absolute number of traffic fatalities is tragically large, the failure rate of human driving is actually very small. A human driver makes a fatal mistake once in about 88 million miles. As a co-founding member of the Stanford Racing Team, we have built several relevant prototypes of autonomous cars. These include Stanley, the winner of the 2005 DARPA Grand Challenge and Junior, the car that took second place in the 2007 Urban Challenge. These prototypes demonstrate that autonomous vehicles can be successful in challenging environments. Nevertheless, reliable, cost-effective perception under uncertainty is a major challenge to the deployment of robotic cars in practice. This dissertation presents selected perception technologies for autonomous driving in the context of Stanford's autonomous cars. We consider speed selection in response to terrain conditions, smooth road finding, improved visual feature optimization, and cost effective car detection. Our work does not rely on manual engineering or even supervised machine learning. Rather, the car learns on its own, training itself without human teaching or labeling. We show this "self-supervised" learning often meets or exceeds traditional methods. Furthermore, we feel self-supervised learning is the only approach with the potential to provide the very low failure rates necessary to improve on human driving performance.