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The MarketWatch News Department was not involved in the creation of this content.

Oct 11, 2022 (Reportmines via Comtex) -- Pre and Post Covid is covered and Report Customization is available.

This "Autonomous Data Platform market" provides strategists, marketers, and senior management with the critical information they need to assess the Autonomous Data Platform market as it emerges from the COVID-19 shutdown. Report on the Autonomous Data Platform market provides a holistic analysis, of Autonomous Data Platform market size and forecast 2022 - 2028, trends, growth drivers, and challenges, as well as vendor analysis. Market segment by types covered On-premises,Cloud.

The global Autonomous Data Platform market size is projected to reach multi million by 2028, in comparision to 2021, at unexpected CAGR during 2022-2028 (Ask for demo Report).

The top players are concentrating mostly on technical developments to increase efficiency. The long-term growth patterns for this Autonomous Data Platform market can be taken by continuing the current development progress and financial strength to participate in the best strategies. The key market players for this market are listed below: Oracle,Teradata,IBM,AWS,MapR,Cloudera,Qubole,Ataccama,Gemini Data,DvSum,Denodo,Zaloni,Datrium,Paxata,Alteryx.

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The top competitors in the Autonomous Data Platform Market, as highlighted in the report, are:

  • Oracle
  • Teradata
  • IBM
  • AWS
  • MapR
  • Cloudera
  • Qubole
  • Ataccama
  • Gemini Data
  • DvSum
  • Denodo
  • Zaloni
  • Datrium
  • Paxata
  • Alteryx

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Market Segmentation

The worldwide Autonomous Data Platform Market is categorized on Component, Deployment, Application, and Region.

The Autonomous Data Platform Market Analysis by types is segmented into:

The Autonomous Data Platform Market Industry Research by Application is segmented into:

  • BFSI
  • Healthcare and Life Sciences
  • Retail
  • Manufacturing
  • Telecommunication and Media
  • Government
  • Others

In terms of Region, the Autonomous Data Platform Market Players available by Region are:

  • North America:
  • Europe:
    • Germany
    • France
    • U.K.
    • Italy
    • Russia
  • Asia-Pacific:
    • China
    • Japan
    • South Korea
    • India
    • Australia
    • China Taiwan
    • Indonesia
    • Thailand
    • Malaysia
  • Latin America:
    • Mexico
    • Brazil
    • Argentina Korea
    • Colombia
  • Middle East & Africa:
    • Turkey
    • Saudi
    • Arabia
    • UAE
    • Korea

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Impact Analysis of COVID 19:

The outbreak of Coronavirus disease (COVID-19) has acted as a massive restraint on the Autonomous Data Platform market as supply chains were disrupted due to trade restrictions and consumption declined due to lockdowns imposed by governments globally. The regional analysis of this report covers the North America: United States, Canada, Europe: GermanyFrance, U.K., Italy, Russia,Asia-Pacific: China, Japan, South, India, Australia, China, Indonesia, Thailand, Malaysia, Latin America:Mexico, Brazil, Argentina, Colombia, Middle East & Africa:Turkey, Saudi, Arabia, UAE, Korea.

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Major Benefits for Industry Participants & Stakeholders:

The fact that coronavirus has stimulated growth in the Autonomous Data Platform industries by creating new security expectations, experimenting with new inventions, and bringing together different stakeholders. In this market research report, the application can be divided into BFSI,Healthcare and Life Sciences,Retail,Manufacturing,Telecommunication and Media,Government,Others. The report is of 191 pages.

The Autonomous Data Platform market research report contains the following TOC:

  • Report Overview
  • Global Growth Trends
  • Competition Landscape by Key Players
  • Data by Type
  • Data by Application
  • North America Market Analysis
  • Europe Market Analysis
  • Asia-Pacific Market Analysis
  • Latin America Market Analysis
  • Middle East & Africa Market Analysis
  • Key Players Profiles Market Analysis
  • Analysts Viewpoints/Conclusions
  • Appendix

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Highlights of The Autonomous Data Platform Market Report

The Autonomous Data Platform Market Size and Industry Challenges:

  • The Autonomous Data Platform market research report offers market size and forecasts in value for all the segments.
  • Based on various inferences made by analysts, the challenges are identified.
  • The final draft will highlight both the challenges faced in the Autonomous Data Platform industry and the companies highlighted in the Autonomous Data Platform market report.

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The Autonomous Data Platform Market Industry Research Report contains:

  • Regulatory scenario, regional dynamics, and Autonomous Data Platform market insights of leading countries in each region
  • Market Segments trend and analysis
  • Graphical Representation of Size, Share, and Trends in Updated Regional Analysis
  • The most recent version of the Autonomous Data Platform market research report covers an analysis of the top Autonomous Data Platform market players, their business strategies, sales volume, and revenue.
  • Facts and Factors in the Autonomous Data Platform Market Research

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Key Reasons for Purchasing the Autonomous Data Platform Market Report:

  • Gain a truly global perspective with the most comprehensive report available on this Autonomous Data Platform market covering geographies
  • Understand how the Autonomous Data Platform market is being affected by the coronavirus and how it is likely to emerge and grow as the impact of the virus abates
  • Create regional and country strategies based on local data and analysis
  • Identify growth segments for investment

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Killexams : Deloitte Launches AI-fueled Expansion of SuperLedger™ Solution at Oracle CloudWorld 2022

Deloitte's autonoumous SuperLedger™ brings together several components of Oracle Cloud along with Deloitte's intellectual property to move finance organizations closer to real-time, touchless operations

NEW YORK, Oct. 13, 2022 /PRNewswire/ -- Deloitte today announced the launch of autonomous SuperLedger™, an AI-fueled expansion of its existing integrated cloud platform for transaction processing, financial planning and analysis (FP&A), and sub-ledger reporting. SuperLedger offers benefits similar to full ERP consolidation that establishes on secure source of the truth, but with greater speed and lower cost.

As used in this document,

Deloitte's autonomous SuperLedger is particularly well-suited for large, complex, organizations that have executed or are undergoing an M&A growth strategy that are looking to enable insightful analysis across multiple ERPs through one comprehensive, secure and cloud SaaS-based digital tool. The solution can be tailored for specific industries, including insurance, health care, technology, media, industrial as well as companies in any sector with high-volume, low-value, non-ERP transactions.

SuperLedger integrates the latest in AI technology and machine learning models to move finance organizations closer to real-time, touchless operations. It includes several enhancements that use Oracle Autonomous Database and Deloitte's proprietary AI capabilities to deliver benefits above and beyond those found in the earlier version of Deloitte's SuperLedger, which provides an integrated cloud platform for transaction processing, FP&A, and sub-ledger reporting without the need for full ERP consolidation. These enhancements include:   

  • Kinetic Start-up — a tool that instantly scans the on-premise environment, extracts relevant data from the legacy systems, and makes it available in the cloud. This allows finance leaders to quickly see the nature of their current data and the degree of transformation that may be required.
  • Touchless processing — pre-built automations for expediting and streamlining common financial processes such as consolidation and close, procure to pay, and FP&A.
  • Self-healing and auto-correction — machine learning models that automatically correct errors in source data based on historic patterns. The models also create an exception-handling framework for instances requiring human intervention.
  • Intuitive recommendations — options for manually correcting errors based on machine analysis.
  • Sensing, detection and prediction — machine learning models that enable continuous risk-sensing and anomaly detection for high-volume, low-value financial transactions as well as predictive forecasting about the future of the business based on select variables.

"Since its introduction in 2019, Deloitte's SuperLedger has been a compelling solution for many organizations, helping them to consolidate their data and systems, streamline their finance processes to drive better reporting and analytics, and deliver better experiences for their employees," said Varun Dhir, principal, Deloitte Consulting LLP. "The expanded autonomous capabilities in this new release take finance transformation up a notch. The solution can help companies to rapidly adopt a modern cloud platform and digitally evolve toward continuous, real-time operations — and in so doing, to better position themselves for growth."

Experience autonomous SuperLedger firsthand

Connect with Deloitte professionals at the Oracle CloudWorld Hub and experience the power of Deloitte's autonomous SuperLedger in person. Our professionals will feature daily theater session presentations on the solution along with personalized demos. Deloitte is pleased to be the Global Sponsor of Oracle CloudWorld in Las Vegas, Oct. 17-20, 2022. This new global conference will bring people together to share ideas, develop in-demand skills and learn about cloud infrastructure and applications.   

About Deloitte

Deloitte provides industry-leading audit, consulting, tax and advisory services to many of the world's most admired brands, including nearly 90% of the Fortune 500® and more than 7,000 private companies. Our people come together for the greater good and work across the industry sectors that drive and shape today's marketplace — delivering measurable and lasting results that help reinforce public trust in our capital markets, inspire clients to see challenges as opportunities to transform and thrive, and help lead the way toward a stronger economy and a healthier society. Deloitte is proud to be part of the largest global professional services network serving our clients in the markets that are most important to them. Building on more than 175 years of service, our network of member firms spans more than 150 countries and territories. Learn how Deloitte's approximately 415,000 people worldwide connect for impact at www.deloitte.com.

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee ("DTTL"), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as "Deloitte Global") does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the "Deloitte" name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Please see www.deloitte.com/about to learn more about our global network of member firms.

 

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SOURCE Deloitte Consulting LLP

Thu, 13 Oct 2022 09:01:00 -0500 en text/html https://markets.businessinsider.com/news/stocks/deloitte-launches-ai-fueled-expansion-of-superledger-solution-at-oracle-cloudworld-2022-1031803819
Killexams : 5G Transition Opens New Opportunities for Autonomous Trucks

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The transition to a 5G wireless network is likely to open up new opportunities in terms of autonomous trucking technology.

“The most critical aspect of connectivity for autonomous vehicles is high levels of reliable connectivity,” said Chris Gutierrez, director of research and development of autonomous systems at Navistar. “The transition to 5G will enable new opportunities to improved coverage and reliability of the existing 4G networks.

"While autonomous vehicles may not require 5G technology in all cases to complete their tasks, the build-out of the new network will provide the opportunities to raise the bar and lessen the burden on the autonomous vehicles.”

Chris Gutierrez

Gutierrez

Navistar is among the truck manufacturers that have focused on developing driverless technologies. That includes partnering with autonomous driving technology company TuSimple to develop a commercial-ready Level 4 fully autonomous driving solution for longhaul heavy-duty trucks. At Level 4, human interaction is not required during operation.

“One of the biggest benefits is that 5G promises lower latency — around 5 milliseconds versus 4G’s range at 60-90ms,” a TuSimple spokesperson said. “With this improved performance, a truck going down the freeway with a reliable connection is able to receive information much quicker. This is one of the more helpful features when compared to older technologies. For example, with lower latency, autonomous remote operation centers in the cloud can get traffic condition change information to the trucks quicker.”

The spokesperson added that another benefit of 5G capability is that upload speeds can be about 30% faster. The performance improvement will offer better visibility for remote operators keeping an eye on the truck when they stream camera and lidar data to the cloud.

“All and all, 5G will become more useful especially when the autonomous fleets start to scale and are more widely available on the road,” the spokesperson said. “Getting ubiquitous wireless coverage remains the most important component regardless of speeds when it comes to connectivity.”

Navistar International truck with TuSimple autonomous technology

A Navistar International truck equipped with TuSimple autonomous technology on display at 2022 CES in Las Vegas. (Joe Buglewicz/Associated Press)

Driverless technology companies are unlikely to rely on mobile networks for primary functions because the autonomous truck systems need to be able to operate safely and independently. Sensors and cameras are more reliable for fast and uninterrupted readings of conditions, but mobile networks may be useful for secondary functions such as sourcing information on traffic and weather.

“Although Plus is developing our autonomous driving technology and products independent of 5G or V2X being available, faster V2X implementation made possible by 5G technology would lead to safer mobility and less traffic congestion,” Plus Chief Operating Officer Shawn Kerrigan said. “This creates a safer road environment for fully autonomous trucks to operate in, in addition to the safety gains that self-driving trucks already bring about on their own.”

Shawn Kerrigan

Kerrigan

Daimler Truck AG and Torc Robotics entered into their fourth year of partnership Oct. 6. Their focus has been on industry collaboration and commercializing of Level 4 autonomous trucks in the U.S. for longhaul applications. Daimler Truck gained a majority share in Torc in 2019.

“The sensors installed in the vehicle [camera, lidar, radar] plus a highly accurate map are responsible for ensuring that the automated vehicle can safely navigate and detect objects,” Daimler Truck and Torc said in a statement. “5G is not absolutely necessary for automated driving. However, in vehicle-to-infrastructure communication, 5G is a promising new technology that we are investigating.”

RoadSigns

TT's Eugene Mulero joins host Mike Freeze to discuss the midterm elections, and what the fight for control of Congress will mean for trucking. Tune in above or by going to RoadSigns.ttnews.com.

The statement also noted that the technology could enable the exchange of messages between vehicles and road infrastructure or other systems. This makes it possible for the system to understand when there is a potential for dangerous situations such as slippery ice on bridges, or traffic information such as sudden stopped traffic behind a hilltop earlier.

“Vehicle-to-X communication thus represents an additional, potential sensor for future development,” the statement continued. “While our current, short-term goals for Level 4 technology do not include 5G integration, we see the opportunity in vehicles that can communicate regardless of their position in order to be able to use it across the board, to provide own relevant information and to get traffic information and warnings, road information about construction sites or weather conditions, map updates, etc.”

AT&T on Feb. 22 became the first major network to make the transition. This represented a major step in cellular service providers shutting down their 3G networks to free wireless spectrum for existing 4G LTE and the still-expanding 5G networks.

“Our autonomous vehicles don’t rely on a constant wireless connection for the vehicle to operate safely and don’t use V2V, 5G or teleoperation/remote driving,” a Waymo spokesperson said. “We may use a cell connection to share supplemental information with the vehicle’s autonomous driving system about conditions on the road, but all the driving decisions are made by our tech — the Waymo Driver — itself, relying primarily on the on-board sensors [lasers, radar, cameras, etc.]. Our approach is based on our belief that an autonomous driving system, which is rigorously tested and has backup systems and redundancies to handle the unexpected, is the safest model.”

Thu, 13 Oct 2022 02:54:00 -0500 en text/html https://www.ttnews.com/articles/5g-transition-opens-new-opportunities-autonomous-trucks
Killexams : Imperva Meets Enterprise and Public Sector Data Security Needs for Critical and Sensitive Workloads on Oracle Cloud Infrastructure

Powered by Oracle Cloud, Imperva Data Security Fabric Helps Secure and Accelerate Migrations to the Cloud

SAN MATEO, Calif., October 13, 2022--(BUSINESS WIRE)--Imperva, Inc., (@Imperva) a cybersecurity leader whose mission is to protect data and all paths to it, announces that Imperva is extending its award-winning, hybrid data security platform to Oracle Cloud Infrastructure (OCI) to help customers simplify migration, and automate compliance monitoring of cloud data instances. Imperva Data Security Fabric (DSF) has achieved Powered by Oracle Cloud Expertise status and is now available on Oracle Cloud Marketplace, offering added value to Oracle Cloud customers.

Imperva DSF provides unified data-centric security controls across the entire data estate offering scalability and simpler infrastructure. Through a single interface, Imperva Data Security Fabric helps discover and protect sensitive data types, including structured, semi-structured, and unstructured data, as enterprise customers migrate from globally dispersed data centers to Oracle Cloud Infrastructure. In addition, Imperva DSF supports several Oracle Database versions including Oracle Database 19c, and Oracle Database 21c, as well as Oracle Autonomous Transaction Processing (ATP) and Oracle Autonomous Data Warehouse (ADW). Imperva is also a member of Oracle Partner Network (OPN).

"The cloud represents a huge opportunity for our partner community," said David Hicks, group vice president, Worldwide ISV Cloud Business Development, Oracle. "Imperva’s commitment to innovation with Oracle Cloud along with knowledgeable execution can help our mutual customers deploy cloud-enabled cybersecurity solutions optimized to meet critical business needs."

"Improving the customer experience is a top business priority driving digital transformation. With customers becoming more attuned to the value of their data and the risks present as a result, organizations need to consider security and data protection as part of this transformation," said Jennifer Glenn, Research Director for the IDC Security and Trust Group. "For Oracle customers considering moving to Oracle Cloud Infrastructure, Imperva Data Security Fabric can provide visibility and automation across each environment, helping protect critical data at each stage of the migration."

"Imperva and Oracle have collaborated for years, helping mutual customers monitor and secure their sensitive data," says Dan Neault, SVP and GM, Data Security, Imperva. "We are excited to share that we have extended our platform to customers migrating their data to OCI with Imperva Data Security Fabric, now available in the Oracle Cloud marketplace."

Security complexity has hindered cloud agility

The cloud has revolutionized IT, offering organizations a strategic accelerator to rapidly pursue new market initiatives and adapt their operations in the face of new business challenges and opportunities. However, uncertainty about how best to overcome security risks and ensure regulatory compliance has slowed cloud adoption historically.

Significant differences between on-premises and cloud database environments have led organizations to try extending traditional database security tools to their cloud environments. Often they encounter unavoidable limitations, from the technical impossibility of installing agents on database as a service (DBaaS) deployments, to the practical limitations of directing all cloud database traffic through a proxy service. This has resulted in organizations using a patchwork of individual tools. This approach raises the likelihood of human error, unnecessarily increasing the risk of a breach or compliance failure.

Automation for many data security and regulatory compliance tasks reduces, and in some cases, may eliminate the burden placed on data security teams to manually keep compliance updates, records, and audit trails. Imperva DSF can help save time and reduce the cost of securing data by unifying security tasks including data activity monitoring, sensitive data discovery, classification, compliance, risk analytics, and threat detection.

Powered by Oracle Cloud, Imperva DSF provides information security leaders with an approach for enabling security, compliance and governance outcomes. Security teams can benefit by simplifying the protection of the organization’s diverse data ecosystem, with single-pane-of-glass administration, integration with other IT security investments, and broad database coverage.

About Powered by Oracle Cloud Expertise

Powered by Oracle Cloud Expertise recognizes OPN members with solutions that run on Oracle Cloud. For partners earning the Powered by Oracle Cloud Expertise, this achievement offers customers confidence that the partner's application is supported by the Oracle Cloud Infrastructure SLA, enabling full access and control over their cloud infrastructure services as well as consistent performance.

Additional Information

About Imperva

Imperva is the comprehensive digital security leader on a mission to help organizations protect their data and all paths to it. Only Imperva protects all digital experiences, from business logic to APIs, microservices, and the data layer, and from vulnerable, legacy environments to cloud-first organizations. Customers around the world trust Imperva to protect their applications, data, and websites from cyber attacks. With an integrated approach combining edge, application security, and data security, Imperva protects companies ranging from cloud-native start-ups to global multi-nationals with hybrid infrastructure. Imperva Threat Research and our global intelligence community keep Imperva ahead of the threat landscape and seamlessly integrate the latest security, privacy, and compliance expertise into our solutions.

About Oracle PartnerNetwork

Oracle PartnerNetwork (OPN) is Oracle’s partner program designed to enable partners to accelerate the transition to cloud and drive superior customer business outcomes. The OPN program allows partners to engage with Oracle through track(s) aligned to how they go to market: Cloud Build for partners that provide products or services built on or integrated with Oracle Cloud; Cloud Sell for partners that resell Oracle Cloud technology; Cloud Service for partners that implement, deploy and manage Oracle Cloud Services; and License & Hardware for partners that build, service or sell Oracle software licenses or hardware products. Customers can expedite their business objectives with OPN partners who have achieved Expertise in a product family or cloud service. To learn more visit: http://www.oracle.com/partnernetwork.

Trademarks

Oracle, Java and MySQL are registered trademarks of Oracle Corporation.

View source version on businesswire.com: https://www.businesswire.com/news/home/20221013005903/en/

Contacts

Press Contact
Erik Kingham
ImpervaPR@imperva.com

Thu, 13 Oct 2022 06:01:00 -0500 en-US text/html https://finance.yahoo.com/news/imperva-meets-enterprise-public-sector-180000058.html
Killexams : Beyond Autonomous Cars

As the automotive industry takes a more measured approach to self-driving cars and long-haul trucks for safety and security reasons, there is a renewed focus on other types of vehicles utilizing autonomous technology.

The list is long and growing. It now includes autonomous trains, helicopters, tractors, ships, submarines, drones, delivery robots, motorcycles, scooters, and bikes, all of which are in various stages of development.

Fig. 1: Autonomous tractors could change agriculture as we know it. Source: John Deere

Fig. 1: Autonomous tractors could change agriculture as we know it. Source: John Deere

Industry consensus says the bulk of the profit, as well as the life-saving potential, lies in developing self-driving consumer cars. However, tech executives say looking at the autonomous ecosystem holistically reveals important takeaways for the hardware community.

“The industry’s focus on autonomous cars is correct from a safety standpoint, given the number of cars being driven and the potential to dramatically decrease road deaths,” said Paul Karazuba, Expedera’s vice president of marketing. “But it also makes sense to look at things like tractors and ships and aircraft for certain levels of autonomy, especially if you can make it safer, more efficient, greener, or whatever the case may be.”

For one thing, autonomous automotive innovations find their way into other autonomous vehicles, and vice versa. And all of those innovations find their way into other industrial and commercial applications.

“Everybody talks a lot about autonomous cars, but it’s actually all modes of transportation that we’re chasing,” said Walt Hearn, vice president of the Ansys Americas sales team, in a podcast earlier this year. “We’re chasing autonomous boats, autonomous airplanes, autonomous trains, because all the sensors are able to be used for all different industries. It’s not just being pushed in automotive. Imagine a lidar and a camera sensor that are on an autonomous car. Now it’s in a manufacturing plant monitoring the robots that are manufacturing products. There are so many applications coming out of autonomy.”

That is partly due to the different financial assumptions for autonomous technology in vehicles other than cars. While keeping production costs as low as possible is a major concern for consumer cars, the calculation is different for other sorts of commercial vehicles. “There are less price, space, and power restrictions on commercial vehicles like trucks, ships, and aircraft,” said Robert Schweiger, group director of automotive solutions at Cadence. “Cost is also of less concern because of the return on investment of operating these vehicles 24/7 and saving labor costs.”

Many of the technical challenges are also easier in other vehicles. “Autonomy feeds on predictability, and really does not like unpredictability,” said Thierry Kouthon, technical product manager at Rambus. “The unpredictability tends to be more on the consumer side. Professional and business operations are more predictable, which is why it’s an interesting way to look at things because it narrows down the complexity of the problem. Autonomy is basically about managing sporadic evidence, and the less sporadic evidence you have to manage, the better the autonomy,” he said.

There are certain technical aspects to autonomy that tend to remain consistent, whether the vehicle is a car or a forklift. Kouton noted that autonomy in vehicles that are traditionally operated by humans means automating or electrifying all the functions that are usually either mechanical, hydraulic, or otherwise. “Instead of having a hand or foot pulling a lever or pressing a pedal, you have a computer that is engaging an actuator. You need a computerized environment to control all these functions, usually an ECU. All these ECUs have to work in harmony to deliver the autonomy, meaning that the brakes have to listen to the engine, which has to listen to the steering.”

Furthermore, AI and ML allow the vehicle to operate in a wide variety of circumstances. Network connectivity is crucial for a wide swath of autonomous vehicles, and is believed to be one of the keys to unlocking L5 fully-autonomous cars. Equally important is what happens when that connectivity suddenly disappears. Through the process known as graceful degradation, self-driving cars must be equipped to pull over to the side of the road and park in the case of connectivity issues. In the case of an air taxi, that means being able to withstand brief connection losses without falling out of the sky.

Autonomous technology is also inherently disruptive to the EDA design cycle, said Neil Hand, director of strategy for design verification technology at Siemens Digital Industries Software. “Autonomous vehicles, whether they be robots, cars, or planes, bring in a whole new set of requirements. It adds new functional safety aspects, or a new focus on non-determinism that has to be managed throughout the flow.”

Much of the disruption comes from the shift from an electromechanical focus to an electrical and software focus, which can be a major transition for vehicle companies that are used to a certain design protocol.

“Momentum and inertia have a huge impact on how the system is designed and what the compute requirements actually are,” said David Fritz, vice president of hybrid-physical and virtual systems for automotive and mil/aero at Siemens. For one thing, a maneuver that might be completely safe for an autonomous vehicle could be inappropriate and possibly lethal to any sort of autonomous vehicle that carries a human inside of it. “It’s designing a vehicle such that it’s cognizant of the fact that not only does the vehicle have momentum, but so do the passengers, and the anatomy of the passengers has something to do with whether or not a decision the vehicle could make is safe for the passengers.”

From a data perspective, autonomous vehicles generate far more data than their less-autonomous counterparts through myriad sensors, cameras, and other data-generating components. That data volume in turn impacts memory, bandwidth requirements, and data center usage.

“Data is no longer generated by human events,” wrote Mike Gianfagna, senior director of enterprise marketing at Synopsys. “Thanks to widespread sensor deployment, coupled with a hyperconnected environment, all types of devices are generating data at an exponentially increasing rate. Your smartwatch captures details about your exercise regimen and your health. According to one study, an autonomous vehicle can generate 5TB of data per hour of operation. If you consider how many such vehicles will be in operation in the coming years, you can clearly see a data avalanche.”

Because of this data explosion, back-end compute and data storage will be a major challenge, as well as a business opportunity for vehicles across the autonomous spectrum, which is more than just cars.

Safety and security
Some of the more glaring differences from a hardware perspective between autonomous cars and trucks compared to other autonomous vehicles are related to safety and security. A robo-taxi without the proper hardware and software is a potential killing machine, but lethality would be a challenge for an autonomous skateboard. More dangerous non-car autonomous vehicles are unlikely to reach the same levels of mass adoption as consumer automotive. They also are less likely to encounter ethical issues that require prioritizing human life. A self-driving car, for example, must consider the well-being of the people inside and outside of the vehicle.

Many autonomous vehicles outside of the consumer automotive industry are unmanned or require only infrequent or remote human participation, noted Frank Schirrmeister, vice president of solutions and business development at Arteris IP. “This significantly streamlines the necessary decision-making and AI the technology necessary to process and execute those decisions. In a car, the system has to realize that someone is likely to get hurt in the next 10 seconds, and then it has a choice between the different people involved. But in many other cases, a vehicle can just self-destruct, and that will save a life.”

But how a vehicle behaves depends on more than just the initial design. Rambus’ Kouthon said high-tech cars are by far the most popular target among commercially available semi-autonomous vehicles, because they are the most common. “Still, the strategy is often similar regardless of the vehicle type. Basically, a hacker can go in and make one ECU believe something is false. For instance, it will make the engine control unit believe that the engine is running at 20 miles per hour, when it is actually at 100 miles per hour, which then creates an accident. Or it can tell the brakes to slam on when the car is running at 80 miles per hour. Those are some of the actions you can take as an adversary if you can access the ECUs — both the unit itself and the communication between the units or the software that runs those units. Products based on cryptography and security engineering can provide some amount of protection.”

By land, sea, or air
When comparing the hardware considerations of autonomous tractors, ships, and aircraft to that of cars, tractors almost always will have a line of sight to the sky and thus be able to make use of GPS, which is not always the case with a car.

“It’s not uncommon in the Financial District in San Francisco to have your car think you’re a block or two away from where you actually are because of the height of the buildings,” Expedera’s Karazuba said. “On the other hand, tractors will encounter wildlife to a degree that most cars will not, will have to operate amid huge clouds of dirt and dust, and also have to be sold to farmers who are used to keeping their tractors for decades and repairing the vehicle hardware themselves.”

Here, a tractor’s slow speed is an advantage technologically, Karazuba said, because it means the vehicle can be less “processor-intensive” than a car traveling highway speeds. Furthermore, there is less data to train tractor AI compared to the massive data sets available for cars.

For autonomous ships and other watercraft, the complexity increases due to the difficulty of enabling the cameras and other sensors to function despite interacting with water, salt, humidity, adverse weather, or even just a moonless night. “When you have a car, you can illuminate the area in front of the car with headlights,” Karazuba said. “On a ship, you can point a light forward but you can’t bathe the entire area like you can with a car. That removes hours a day of visibility depending on where you are on the globe. The same is true for aircraft.”

Conclusion
While the industry is heavily invested in autonomous automotives from both a life-saving and profit-generating standpoint, the ecosystem of semi-autonomous vehicles goes far beyond self-driving cars.

“It’s very much like the race to the moon,” said Siemens’ Fritz. “We created so many technologies that at the time were specifically to get our people to the moon, but since then have branched out to change our lives in so many different ways.”

Fritz pointed to a wireless security camera that is powered by a solar panel and is connected to a cellular system, a product similar to the machines being implemented or considered in various smart city projects. The camera has the ability to differentiate between a human, animals, and other moving objects like a leaf, which speaks to the progress made in AI object detection and classification. He also noted that OLED films can be placed inside the windows of a vehicle to create the feeling of being immersed in an environment far different than the real one outside. “None of us can predict the future, other than the fact that it’s going to be very different, because of all these technologies that are being pioneered now for autonomous vehicles,” he said.


Wed, 12 Oct 2022 12:33:00 -0500 en-US text/html https://semiengineering.com/beyond-autonomous-cars/
Killexams : Transforming manufacturing with autonomous supply chains

Digital technologies offer huge opportunities for supply chain transformation. Professor Alexandra Brintrup from the Institute for Manufacturing (IfM), University of Cambridge, explains how agent-based systems that use AI and the Internet of Things (IoT) are helping to make supply chains smarter and more efficient

Professor Alexandra Brintrup from the Institute for Manufacturing (IfM), University of Cambridge

Supply chains can be large scale and complex, often consisting of multiple interacting companies that don’t always share the same information systems. This can lead to a lack of real-time, traceable information across the supply chain, making operations inefficient and making them vulnerable to coordination challenges and disruptions (as seen during COVID-19).

Without visibility of this kind of information, it is difficult to plan for, or respond to, problems such as volatile market demand, supplier delays or driver shortages.

However, new technologies such as AI and IoT offer the opportunity for companies to transform supply chains into autonomous supply chain ecosystems, resulting in more seamless and coordinated operations; with better information sharing and improved decision making.

Autonomous supply chains

By combining artificial intelligence with IoT, a software ‘agent’ can act on behalf of a stakeholder along the supply chain. Agents can be programmed to use algorithms to share data, predict outcomes and even negotiate with other agents, so they can pre-empt disruptions with timely interactions such as adjusting order quantities and lead times.

For example, agents can optimise truck loads – even at short notice – saving energy and making operations more sustainable and resilient. The hassle of integrating another IT system is also removed as agents can be added onto existing systems.

Autonomous supply chains can therefore help reduce labour costs and enhance operating efficiency by automating routine tasks of supply chain monitoring. By adjusting orders and learning to negotiate between different parties in the supply chain, autonomous supply chains can connect information both up- and downstream.

Another benefit of adopting this approach is that it helps open up the market to everyone, from small- and medium-sized companies to large multinationals. The value gained, thanks to better efficiency, is shared across the whole supply chain.

Are autonomous supply chains feasible?

Although some manufacturers have invested in developing the technology, there are currently very few industrial adopters of autonomous supply chains. Whether or not the manufacturing industry can integrate digital technologies and establish autonomy throughout the supply chain remains to be seen.

Working with local Cambridge tech company Fetch.ai to assess the technical feasibility of autonomous supply chains in real-life, our team from the Supply Chain AI Lab at the IfM has developed two autonomous supply chain demonstrators. These present feasible and scalable solutions by showing how IoT, multiagent systems and AI can work together to create autonomous, decentralised operations in supply chains.

With Fetch.ai, we were able to use the company’s new decentralised Autonomous Agent Framework (AEA) and corresponding technical support to implement a prototype using their agent development technology.

Using a mock scenario which mimics the supply chain of a meat company which purchases meat wholesale and supplies to local restaurants, we wanted to solve the need for rapid, secure exchange of data over the end-to-end of the supply chain.



This first demonstrator showcases how sensor data can be shared between organisational boundaries on a food logistics chain so that product quality can be guaranteed and traced. Some of the capabilities of the system include: the automatic selection of suppliers, the use of IoT in monitoring the ambient conditions of the transport vehicles, rerouting when unforeseen events occur (traffic jams, for example), and the provision of an analytic summary about product quality.

Building on this, we developed a second demonstrator which streamlined the supply chain automation pipeline and integrated an agent-based autonomous supply chain management platform, including procurement, transport monitoring, negotiation, inventory update, and product quality summary.

The two demonstrators showcase how agent-based technology can integrate with IoT, AI, visualisation and web interfaces to form a prototype system for automating many mundane tasks in supply chains. They offer intuitive and tangible interpretations of key autonomous supply chain components to industrial stakeholders.



New opportunities for business

Autonomous supply chains offer new opportunities for businesses, helping them to understand their success in improving performance and helping supply chains act with immediacy and decisiveness.

We believe autonomous supply chains could be transformational for businesses, helping them to create better visibility and traceability, and automating routine operations without investing in integrated, expensive platforms.

Although integration with existing systems remain a barrier to adoption, robust platforms are emerging and have the possibility to make agent-based supply chains a reality.

Key takeaways:

  • Digital technologies offer huge opportunities for supply chain transformation
  • New technologies such as AI and IoT offer the opportunity for companies to transform linear supply chains into autonomous supply chain ecosystems, resulting in more seamless and coordinated operations
  • Software ‘agents’ can act on behalf of a stakeholder along the supply chain
  • IfM researchers have used ‘real-life’ scenarios to test the viability of autonomous supply chains resulting in better visibility and traceability

Read more of our Automation articles here

Sun, 09 Oct 2022 12:00:00 -0500 en-GB text/html https://www.themanufacturer.com/articles/transforming-manufacturing-with-autonomous-supply-chains/
Killexams : SailGP charts new course into Web3

If there is an age at which words such as ‘blockchain’ and ‘cryptocurrency’ make you feel old, you make want to skip this Forbes story:


SailGP—dubbed the sailing world’s equivalent of Formula One—is navigating into the Web3 space.

SailGP is aiming to have a professional sailing team owned and operated by a decentralized autonomous organization, or DAO, that complies with the U.S. securities law, Russell Coutts said in an interview in Singapore on the sideline of the Forbes Global CEO Conference. Coutts, a five-time America’s Cup winner, cofounded SailGP with Oracle’s billionaire chairman and CTO Larry Ellison in 2018.

Their plan for a DAO, which will be run by its members and built on blockchain platform NEAR, is set to issue both security tokens and fan tokens. The security tokens represent ownership in the sailing team and will be made available to 2,000 accredited investors, whereas people who buy the fan tokens will be entitled to vote on team decisions.

The DAO that meets SailGP’s entry requirements is expected to be formed as early as this month, Coutts said. The community-led organization will need to decide whether to establish a new team or purchase an existing one out of the current nine national teams.

“We believe this will be the first fully DAO-operated team under the U.S. securities framework,” said Coutts, CEO of SailGP. The team could join the championship as soon as June 2023 when the fourth season kicks off. – Full report


SailGP informationYouTubeHow to watch

2022-23 SailGP Season 3 Schedule
May 14-15, 2022 – Bermuda Sail Grand Prix presented by Hamilton Princess
June 18-19, 2022 – United States Sail Grand Prix | Chicago at Navy Pier
July 30-31, 2022 – Great Britain Sail Grand Prix | Plymouth
August 19-20, 2022 – ROCKWOOL Denmark Sail Grand Prix | Copenhagen
September 10-11, 2022 – France Sail Grand Prix | Saint-Tropez
September 24-25, 2022 – Spain Sail Grand Prix | Andalucía – Cádiz
November 12-13, 2022 – Dubai Sail Grand Prix presented by P&O Marinas
January 13-14, 2023 – Singapore Sail Grand Prix
February 17-18, 2023 – Australia Sail Grand Prix | Sydney
March 17-18, 2023 – New Zealand Sail Grand Prix | Christchurch
May 6-7, 2023 – United States Sail Grand Prix | San Francisco (Season 3 Grand Final)

Format for 2022-23 SailGP events:
• Teams compete in identical F50 catamarans.
• Each event runs across two days.
• There are three qualifying races each day for all nine teams.*
• The top three teams from qualifying advance to a final race for the event championship and to earn the largest share of the $300,000 USD prize money being split among the top three teams.
• The season ends with the Grand Final, which includes the Championship Final Race – a winner-takes-all match race for the $1m USD prize.
* Qualifying schedule increased from five to six races at France SailGP.

For competition documents, click here.

Established in 2018, SailGP seeks to be an annual, global sports league featuring fan-centric inshore racing in some of the iconic harbors around the globe. Rival national teams compete in identical F50 catamarans for event prize money as the season culminates with a $1 million winner-takes-all match race.

Mon, 17 Oct 2022 06:58:00 -0500 en-US text/html https://www.sailingscuttlebutt.com/2022/10/15/sailgp-charts-new-course-into-web3/
Killexams : Autonomous Delivery Robots: The Precursor To Self-Driving Cars

Ritukar Vijay is the CEO and co-founder of Ottonomy.io — a delivery robotics company.

Cut from the same creative cloth, driverless cars and last-mile delivery robots share many similarities—but only last-mile delivery robots operate on the roads right now. The implementation of self-driving cars is sliding into higher gear, but widespread commercial use is still years away.

“Up to 15 percent of new cars sold in 2030 could be fully autonomous,” says a McKinsey report on AV adoption. But let’s look at how far the AV industry has come.

A Brief History Of The Race To Self-Driving Cars

The major development of autonomous vehicles started in 2004 when the U.S. government sponsored a road rally known as the DARPA Grand Challenge which was held over 150 miles in the Mojave Desert.

The road race was similar to traditional auto races, except for one unique wrinkle—all the entrants must be driverless vehicles, with absolutely no human intervention. So-called “autonomous” vehicles were built, bolts and all, by a disparate group of competitors including engineering students from Carnegie Mellon University, Virginia Tech and Stanford University.

Facing a litany of both engineering and weather-related mishaps, including dust storms, unexpected run-ins with cactuses, jammed breaks and wayward satellite navigation equipment, the race still proved successful. More importantly, the DARPA derby lit the fuse for the autonomous vehicle industry—an industry that has accelerated in the past decade with the arrival of artificial intelligence and a burgeoning autonomous last-mile delivery vehicle market.

Google ran a stealth project known as Project Chauffeur for almost two years, and then Google publicly got into the game with autonomous cars in 2012. Later it was joined by car manufacturers and ride-hailing companies like Uber and Lyft.

Delivery Robots: A Humble Start

Around the same time in 2016, Estonia-based Starship Technologies launched delivery robots that are continuously monitored by human controllers who can take over the control remotely at any time. On the same line, Kiwibot, Coco and others started remotely operated delivery robots, validating the need for such technology.

This signaled the robots as the best and most commercially viable use case for deliveries, but the core value proposition is to have these robots autonomous—not in the future, but now.

A New Age For Delivery Robots

Over the last few years, companies such as Nuro, Yandex, Serve Robotics and my company, Ottonomy.IO, have upped the ante on providing deliveries to users using fully autonomous last-mile robots.

Nuro chose a path to be on roads under 25 mph. The underlying technology circles around full autonomy. Being on the road with these driverless vehicles, the regulatory roadblocks will make the mass adoption a minimum of three to five years (or even more) in the future. Companies like Yandex, Serve Robotics and Ottonomy.IO chose the “autonomous-first” approach.

According to the market research firm Fact.MR, the worldwide autonomous last-mile delivery market will grow by an annual rate of 19% through 2031. As per the report: “The market has seen significant expansion in recent years due to developments in delivery system technology. The market’s expansion can be linked to the growing use of autonomous vehicles to deliver goods without the need for human intervention.”

How Delivery Robots Pave The Way For Autonomous Vehicles

Yes, the underlying technology is similar, from perception to path planning and mapping. Although the delivery robots are low in speed, they often engage in more complicated, unstructured scenarios and pedestrian interactions than autonomous cars. On top of that, autonomous delivery robots are solving a pressing current problem in the supply chain around labor shortages resulting in a huge enterprise push.

That said, here are a few areas where autonomous delivery robots are acting as a precursor to autonomous cars.

Large Fleets

The nuances of running an autonomous fleet of 10 vehicles compared to a fleet of 200 to 2,000 delivery robots is a huge shift in AV operations. The educational benefits of autonomous robot fleets include and are not limited to autonomous fleet management, distributed AV information and processing, fallback safety, fleet orchestration and more.

Edge Cases And Associated Behaviors

Because more delivery robots are out in the market, they are more exposed to a variety of edge cases. Sometimes, sidewalks, airports and curbsides are more challenging than lane-guided roads for autonomous cars, and that results in further advancements in active perception, situational awareness, and autonomous behaviors and safety (in general).

Familiarity With Autonomous Systems

While accessing the packages by delivery robots, the end customers are also more aware and accepting of self-driving technology compared to the apprehensions and risks around self-driving cars.

The Rules Of The Road

Increasingly, regulatory issues have become a challenge. According to the Center for Strategic & International Studies, the NHTSA will take good time to establish regulations for high-speed autonomous cars, further pushing back the day consumers step into a driverless car and use it as transport.

At the same time, more than 20 states in the U.S. have regulations on sidewalk delivery robots. In comparison, only a few states have defined or limited operational permits for self-driving (no-driver) vehicles. It’s no wonder why the adoption of autonomous delivery robots is high. In a nutshell, autonomy in these delivery robots is a critical step toward larger autonomous vehicles.

Final Take

With the massive uptake of delivery robots and less human intervention for last-mile and curbside deliveries, the overall need for autonomous technology is critical.

While autonomous delivery robots are solving some pressing problems of labor shortages and high delivery costs, they are also paving the way for autonomous car adoption as the end consumers become more familiar with the technology. And the same applies to the regulatory framework to evolve before we see a changed landscape by 2030 and autonomous mobility touching our lives.


Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?


Sun, 09 Oct 2022 22:42:00 -0500 Ritukar Vijay en text/html https://www.forbes.com/sites/forbestechcouncil/2022/10/10/autonomous-delivery-robots-the-precursor-to-self-driving-cars/
Killexams : Is an Autonomous Vehicle Patent War Revving Up?

“The emergence of new technologies, or new uses for old technologies, is often closely followed by the purveyors of those technologies using patents to try to exclude or slow down their competitors. In which case, perhaps we are at the starting line of yet another patent war.”

autonomous vehicle

autonomous vehicle

Autonomous vehicles are paving the way as the next big innovation in personal transportation. With new technology, first comes the excitement of breakthroughs in any industry. Then comes the patent litigation arguments over who owns the technology and who can profit off the patents related to the technology. We are seeing this pattern again and perhaps the beginning of the self-driving cars patent wars. Earlier this year, the U.S. Court of Appeals for the Federal Circuit upheld the patentability of all challenged claims in a patent held by Velodyne LiDAR, Inc., one of just a handful of companies that makes LiDAR (light detection and ranging) systems for self-driving cars.

Old Technology, New Possibilities

LiDAR is similar in purpose to radar or sonar but uses light instead of radio or sound waves. It has been around, at least in concept, since the 1960s and is used today in markets such as robotics, industrial, and intelligent infrastructure. For autonomous vehicles specifically, LiDAR is generally used to make high-resolution maps allowing the vehicle to “see” its surroundings. To do so, the LiDAR system sends out pulses of light across the area around the car and measures the time it takes for those pulses to bounce off an object and to return the reflected light. Because the speed of light is known, the LiDAR system can calculate the distance between the object that the light reflected off and the vehicle.

Using this information, the system generates an image or a “point cloud” that informs the vehicle of its surroundings including what objects are near and the distance. Almost every company looking into self-driving vehicles currently uses LiDAR, including Waymo, Chevy Cruise, Magna International, and Motional, Inc. Even Tesla Motors, an outspoken critic of LiDAR has tested the technology in its self-driving cars, with those tests showing LiDAR substantially out-performed Tesla’s standard camera-based system. Velodyne, Luminar, Aeva, Aurora, and Ouster have all joined the race for market share in the LiDAR industry, a market that is expected to grow substantially as autonomous vehicles get on the road.

The Dispute

Velodyne appears to be the first LiDAR company to ask the courts to play a part in that race. Velodyne has filed patent infringement complaints with both the U.S. International Trade Commission and the U.S. District Court for the Northern District of California against fellow LiDAR supplier and competitor, Ouster. Velodyne’s ITC complaint asks that the Commission investigate whether Ouster is violating section 337 of the Tariff Act of 1930 by importing into the United States Ouster’s rotational LiDAR devices, components, and products that are alleged to infringe Velodyne’s patent, as well as its newer 9,983,297 patent.

According to Velodyne’s ITC complaint, Ouster and its contract manufacturer Benchmark Electronics, Inc., “took Velodyne’s revolutionary inventions and incorporated them into Ouster’s competing products” including Oster’s rotating 3-D LiDAR devices and sensing systems and is now manufacturing the majority of these products in Thailand for importation into the United States. Velodyne accuses Ouster of studying Velodyne’s patented technology and products when creating its own products, such as the OS1, as evidenced by Ouster’s own patent (U.S. Patent No. 10,063,849) disclosures acknowledging Velodyne’s patent (U.S. Patent No. 7,969,558) as “the fundamental technology” behind the invention and citing to multiple of Velodyne’s own rotational LiDAR products. If Velodyne is successful before the ITC, it could result in an order preventing Ouster from importing any of its accused products into the United States.

A Potentially Pivotal Moment

It is uncertain whether Velodyne will assert its patent portfolio against other competitors, or if its complaint against Ouster is just a one-off drag race. Regardless, the emergence of new technologies, or new uses for old technologies, is often closely followed by the purveyors of those technologies using patents to try to exclude or slow down their competitors. In which case, perhaps we are at the starting line of yet another patent war. Given the small number of LiDAR providers currently in the market and the apparent necessity of the technology to self-driving cars, this could be a pivotal moment for the industry.

Image Source: Deposit Photos
Image ID: 29760701
Author: iqoncept 

Thu, 13 Oct 2022 23:16:00 -0500 en text/html https://www.ipwatchdog.com/2022/10/14/autonomous-vehicle-patent-war-revving/id=152139/
Killexams : Global Autonomous Farm Equipment Market to Reach $155.1 Billion by 2027

ReportLinker

Abstract: What’s New for 2022?? Global competitiveness and key competitor percentage market shares. Market presence across multiple geographies - Strong/Active/Niche/Trivial.

New York, Oct. 12, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Autonomous Farm Equipment Industry" - https://www.reportlinker.com/p05818534/?utm_source=GNW

Online interactive peer-to-peer collaborative bespoke updates

Access to our digital archives and MarketGlass Research Platform

Complimentary updates for one yearGlobal Autonomous Farm Equipment Market to Reach $155.1 Billion by 2027
- In the changed post COVID-19 business landscape, the global market for Autonomous Farm Equipment estimated at US$65.7 Billion in the year 2020, is projected to reach a revised size of US$155.1 Billion by 2027, growing at aCAGR of 13.1% over the period 2020-2027. Partially Autonomous, one of the segments analyzed in the report, is projected to record 11.7% CAGR and reach US$99.3 Billion by the end of the analysis period. Taking into account the ongoing post pandemic recovery, growth in the Fully Autonomous segment is readjusted to a revised 15.8% CAGR for the next 7-year period.
- The U.S. Market is Estimated at $19.9 Billion, While China is Forecast to Grow at 16.7% CAGR
- The Autonomous Farm Equipment market in the U.S. is estimated at US$19.9 Billion in the year 2020. China, the world`s second largest economy, is forecast to reach a projected market size of US$27.8 Billion by the year 2027 trailing a CAGR of 16.7% over the analysis period 2020 to 2027. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at 9.9% and 10.9% respectively over the 2020-2027 period. Within Europe, Germany is forecast to grow at approximately 11.3% CAGR.

Select Competitors (Total 66 Featured)
AGCO Corporation
AgEagle Aerial Systems Inc.
Autonomous Solutions, Inc.
Autonomous Tractor Corporation
Bobcat Company
Claas KGaA GmbH
Clearpath Robotics Inc.
CNH Industrial N.V.
Case IH
Deere & Company
Kinze Manufacturing Inc.
KUBOTA Corporation
Naïo Technologies
Rowbot Systems
Yanmar Holdings Co., Ltd.

Read the full report: https://www.reportlinker.com/p05818534/?utm_source=GNW

I. METHODOLOGY

II. EXECUTIVE SUMMARY

1. MARKET OVERVIEW
Impact of COVID-19 Pandemic and Looming Global Recession
2020: A Year of Disruption & Transformation
As the Race between the Virus & Vaccines Intensifies, Where is
the World Economy Headed in 2021?
World Economic Growth Projections (Real GDP, Annual % Change)
for 2020 through 2022
Pandemic Slows Down Demand for Agricultural Equipment
COVID-19 Related Issues Impacting Agriculture Industry
COVID-19 Effect on Supply Chain & Shift to Automation Hail New
Era for Autonomous Vehicle & Equipment Makers
Autonomous Farm Equipment - Global Key Competitors Percentage
Market Share in 2022 (E)
Autonomous Farm Equipment Competitor Market Share Scenario
Worldwide (in %): 2020E
Competitive Market Presence - Strong/Active/Niche/Trivial for
Players Worldwide in 2022 (E)
An Introduction to Autonomous Farm Equipment
Types of Autonomous Farm Equipment
Benefits of Autonomous Machinery to Farmers
Global Market Prospects & Outlook
Autonomous Tractors to Experience High Growth
Developed Regions Lead, Developing Economies Poised for High
Growth
Competition
Autonomous Farm Equipment Competitor Market Share Scenario
Worldwide (in %): 2020
Recent Market Activity

2. FOCUS ON SELECT PLAYERS

3. MARKET TRENDS & DRIVERS
Growing Population & Rising Food Security Concerns Drive Demand
for Autonomous Tractors
Long-term Focus on Feeding the World?s Expanding Population to
Sustain the Growth of Mechanized Farm Equipment: World
Population (in Thousands) by Geographic Region for the Years
2019, 2030, 2050, 2100
Food Demand Growth Worldwide: Demand Growth in Million Tonnes
for Select Foods for the Period 2008-2017 and 2018-2027
Climate Change Adds Fuel to the Already Burning Issue of Food
Security
Negative Impact of Rising Surface Temperatures on Agriculture
Industry Increases Reliance on Advanced Farming Technologies
to Sustain Crop Production: Average Global Surface
Temperature (In Degrees Fahrenheit) for the Years 1940, 2000
and 2020
Increasing Mechanization of Agricultural Operations Boosts
Autonomous Farm Equipment Market
Percentage of Mechanization in Agriculture in Select Countries
Declining Agricultural Land Productivity Sets the Stage for
Transformation in Farming
Global Availability of Arable Land in Hectares Per Person for
the Years 1990, 2000, 2018 and 2020
Arable Land Worldwide as a % of Total Land Area for the Years
1990, 2000, 2018 and 2020
Growing Investments in Smart Farming & Increased Use of IoT in
Agriculture Preps the Market for Robust Growth
Rise of Smart Agriculture Sets the Stage for Adoption of
Automated Farming Techniques: Global Smart Agriculture Market
Worldwide (In US$ Billion) by Region/Country for the Years
2020 & 2027
Increased Deployment of IoT in Agriculture Strengthens the
Business Case for Autonomous Farm Equipment: Global
Agricultural IoT Market (In US$ Billion) for the Years 2020,
2023 & 2026
Innovation in Advanced Robotics Plays a Key Role in the
Commercialization of Autonomous Farm Equipment
Continuous R&D in Robotics & the Ensuing Expansion of the
Robotics Market Fuels Market for Agricultural Robotic
Solutions: Global Market for Agricultural Robotics (In US$
Million) for the Years 2020, 2022 & 2024
Autonomous Robotics Find Growing Use in Farms
GPS Systems & Navigation Emerge to Be Indispensable in
Autonomous Farm Equipment
Technology Penetration of High-Precision GPS Vital in Making
Autonomous Vehicles a Reality: Global Positioning Systems:
( GPS) Market Worldwide (In US$ Billion) for the Years 2019,
2022 and 2025
ISOBUS ISO11783 Standard Streamlines Development of Smart
Farming Equipment
Developments in Artificial Intelligence (AI), Machine Vision &
Machine Learning Remain Critical to Commercialization & Growth
Emerging Opportunities for AI in Agriculture to Push Up the
Innovation Index in the Market: Artificial Intelligence (AI)
in Agriculture Worldwide (In US$ Million) for the Years 2019,
2022 and 2025
Rising Significance of Precision Agriculture: Potential
Opportunities for Autonomous Farm Equipment
Global Precision Farming Market (in US$ Million) for the Years
2020, 2022, 2024 and 2026
Global Precision Farming Market by Application (in %) for 2020
Government Budgets for Autonomous Agricultural Technology in
Support of Food Security Goals to Benefit Market Growth
Backed by Government Funding & Support, Automation in
Agriculture Gains Ground: Percentage Share (%) of Government
Expenditure on Agriculture in Total Budgets by Region for the
Years 2010, 2015, 2019 and 2021
Worsening Labor Shortages & Rising Labor Costs Drives Interest
in Autonomous Equipment
Shrinking Labor for Agriculture Raises the Need for Automation:
Percentage (%) of Agricultural Workers in the Global Workforce
for the Years 1992, 2019 & 2022
Contracting Agricultural Labor Drives Demand for Autonomous
Farm Equipment: Agricultural Employment as % of Total
Employment for the Period 2000-2020
Real Wages of Hired Farmworkers in the US (in $ Per Hour) for
the Period 2002-2019
Growing Farm Sizes and Increase in Corporate Farming Raises
Importance of Autonomous Farm Equipment
Farm Consolidation & Increase in Average Size of Farm Pave the
Way for the Deployment of Autonomous Farm Equipment:
Percentage Breakdown of Farm Land by Size and Region
As a Bridge to Fully Autonomous Vehicles, Partially Autonomous
Vehicles Enjoy Significant Market Dominance
Driverless Tractors: The Future of Farming on Large Farmlands
Efficiency and Productivity Benefits of Autonomous Tractors
Translate into Better Crop Yields, Driving Market
Autonomous Vineyard Tractors to Provide Assistance to Growers
Increasing Need to Minimize Greenhouse Gas Emissions Fuel
Demand for Sustainable Autonomous Tractors
Advances in Autonomous Tractor Technologies to Fuel Market
Prospects
Agricultural Drones & Robots Emerge to Revolutionize Farming in
the 21st Century
Expanding Applications of Commercial Drones Supported by
Progressive Improvements in Functionality to Help Autonomous
Farm Equipment Cross the Chasm Between Early Adoption to Mass
Adoption: Global Commercial Drone Market (In US$ Billion)
for the Years 2019, 2022 and 2024
Myriad Benefits Drive the Popularity of Automated Harvesters
Grain Loss During Harvesting Caused by Current Generation
Machines Drives Demand for Smart Autonomous Harvesting
Machines: Corn Head Kernel Loss While Harvesting With a
Combine Harvester
Technological Advancements in Autonomous Technologies to Boost
Market Prospects
Tractor Autopilot for Enhancing Farmer Productivity & Efficiency
Key Challenges Facing Autonomous Farm Equipment Market

4. GLOBAL MARKET PERSPECTIVE
Table 1: World recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Geographic Region - USA, Canada,
Japan, China, Europe, Asia-Pacific and Rest of World Markets -
Independent Analysis of Annual Sales in US$ Million for Years
2020 through 2027 and % CAGR

Table 2: World 7-Year Perspective for Autonomous Farm Equipment
by Geographic Region - Percentage Breakdown of Value Sales for
USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of
World Markets for Years 2021 & 2027

Table 3: World recent Past, Current & Future Analysis for
Partially Autonomous by Geographic Region - USA, Canada, Japan,
China, Europe, Asia-Pacific and Rest of World Markets -
Independent Analysis of Annual Sales in US$ Million for Years
2020 through 2027 and % CAGR

Table 4: World 7-Year Perspective for Partially Autonomous by
Geographic Region - Percentage Breakdown of Value Sales for
USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of
World for Years 2021 & 2027

Table 5: World recent Past, Current & Future Analysis for Fully
Autonomous by Geographic Region - USA, Canada, Japan, China,
Europe, Asia-Pacific and Rest of World Markets - Independent
Analysis of Annual Sales in US$ Million for Years 2020 through
2027 and % CAGR

Table 6: World 7-Year Perspective for Fully Autonomous by
Geographic Region - Percentage Breakdown of Value Sales for
USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of
World for Years 2021 & 2027

Table 7: World recent Past, Current & Future Analysis for
Tractors by Geographic Region - USA, Canada, Japan, China,
Europe, Asia-Pacific and Rest of World Markets - Independent
Analysis of Annual Sales in US$ Million for Years 2020 through
2027 and % CAGR

Table 8: World 7-Year Perspective for Tractors by Geographic
Region - Percentage Breakdown of Value Sales for USA, Canada,
Japan, China, Europe, Asia-Pacific and Rest of World for Years
2021 & 2027

Table 9: World recent Past, Current & Future Analysis for
Harvesters by Geographic Region - USA, Canada, Japan, China,
Europe, Asia-Pacific and Rest of World Markets - Independent
Analysis of Annual Sales in US$ Million for Years 2020 through
2027 and % CAGR

Table 10: World 7-Year Perspective for Harvesters by Geographic
Region - Percentage Breakdown of Value Sales for USA, Canada,
Japan, China, Europe, Asia-Pacific and Rest of World for Years
2021 & 2027

Table 11: World recent Past, Current & Future Analysis for
Other Product Types by Geographic Region - USA, Canada, Japan,
China, Europe, Asia-Pacific and Rest of World Markets -
Independent Analysis of Annual Sales in US$ Million for Years
2020 through 2027 and % CAGR

Table 12: World 7-Year Perspective for Other Product Types by
Geographic Region - Percentage Breakdown of Value Sales for
USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of
World for Years 2021 & 2027

III. MARKET ANALYSIS

UNITED STATES
Autonomous Farm Equipment Market Presence - Strong/Active/
Niche/Trivial - Key Competitors in the United States for 2022
(E)
Autonomous Farm Equipment Market in the US: An Overview
Driverless Tractors Poised for Growth in the Long Run
Table 13: USA recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Type - Partially Autonomous and
Fully Autonomous - Independent Analysis of Annual Sales in US$
Million for the Years 2020 through 2027 and % CAGR

Table 14: USA 7-Year Perspective for Autonomous Farm Equipment
by Type - Percentage Breakdown of Value Sales for Partially
Autonomous and Fully Autonomous for the Years 2021 & 2027

Table 15: USA recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 16: USA 7-Year Perspective for Autonomous Farm Equipment
by Product Type - Percentage Breakdown of Value Sales for
Tractors, Harvesters and Other Product Types for the Years 2021 &
2027

CANADA
Table 17: Canada recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Type - Partially Autonomous and
Fully Autonomous - Independent Analysis of Annual Sales in US$
Million for the Years 2020 through 2027 and % CAGR

Table 18: Canada 7-Year Perspective for Autonomous Farm
Equipment by Type - Percentage Breakdown of Value Sales for
Partially Autonomous and Fully Autonomous for the Years 2021 &
2027

Table 19: Canada recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 20: Canada 7-Year Perspective for Autonomous Farm
Equipment by Product Type - Percentage Breakdown of Value Sales
for Tractors, Harvesters and Other Product Types for the Years
2021 & 2027

JAPAN
Autonomous Farm Equipment Market Presence - Strong/Active/
Niche/Trivial - Key Competitors in Japan for 2022 (E)
Market Overview
Table 21: Japan recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Type - Partially Autonomous and
Fully Autonomous - Independent Analysis of Annual Sales in US$
Million for the Years 2020 through 2027 and % CAGR

Table 22: Japan 7-Year Perspective for Autonomous Farm
Equipment by Type - Percentage Breakdown of Value Sales for
Partially Autonomous and Fully Autonomous for the Years 2021 &
2027

Table 23: Japan recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 24: Japan 7-Year Perspective for Autonomous Farm
Equipment by Product Type - Percentage Breakdown of Value Sales
for Tractors, Harvesters and Other Product Types for the Years
2021 & 2027

CHINA
Autonomous Farm Equipment Market Presence - Strong/Active/
Niche/Trivial - Key Competitors in China for 2022 (E)
Table 25: China recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Type - Partially Autonomous and
Fully Autonomous - Independent Analysis of Annual Sales in US$
Million for the Years 2020 through 2027 and % CAGR

Table 26: China 7-Year Perspective for Autonomous Farm
Equipment by Type - Percentage Breakdown of Value Sales for
Partially Autonomous and Fully Autonomous for the Years 2021 &
2027

Table 27: China recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 28: China 7-Year Perspective for Autonomous Farm
Equipment by Product Type - Percentage Breakdown of Value Sales
for Tractors, Harvesters and Other Product Types for the Years
2021 & 2027

EUROPE
Autonomous Farm Equipment Market Presence - Strong/Active/
Niche/Trivial - Key Competitors in Europe for 2022 (E)
Autonomous Tractors Market in Europe: Long-term Prospects
Remain Positive
Table 29: Europe recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Geographic Region - France,
Germany, Italy, UK and Rest of Europe Markets - Independent
Analysis of Annual Sales in US$ Million for Years 2020 through
2027 and % CAGR

Table 30: Europe 7-Year Perspective for Autonomous Farm
Equipment by Geographic Region - Percentage Breakdown of Value
Sales for France, Germany, Italy, UK and Rest of Europe Markets
for Years 2021 & 2027

Table 31: Europe recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Type - Partially Autonomous and
Fully Autonomous - Independent Analysis of Annual Sales in US$
Million for the Years 2020 through 2027 and % CAGR

Table 32: Europe 7-Year Perspective for Autonomous Farm
Equipment by Type - Percentage Breakdown of Value Sales for
Partially Autonomous and Fully Autonomous for the Years 2021 &
2027

Table 33: Europe recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 34: Europe 7-Year Perspective for Autonomous Farm
Equipment by Product Type - Percentage Breakdown of Value Sales
for Tractors, Harvesters and Other Product Types for the Years
2021 & 2027

FRANCE
Autonomous Farm Equipment Market Presence - Strong/Active/
Niche/Trivial - Key Competitors in France for 2022 (E)
Table 35: France recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Type - Partially Autonomous and
Fully Autonomous - Independent Analysis of Annual Sales in US$
Million for the Years 2020 through 2027 and % CAGR

Table 36: France 7-Year Perspective for Autonomous Farm
Equipment by Type - Percentage Breakdown of Value Sales for
Partially Autonomous and Fully Autonomous for the Years 2021 &
2027

Table 37: France recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 38: France 7-Year Perspective for Autonomous Farm
Equipment by Product Type - Percentage Breakdown of Value Sales
for Tractors, Harvesters and Other Product Types for the Years
2021 & 2027

GERMANY
Autonomous Farm Equipment Market Presence - Strong/Active/
Niche/Trivial - Key Competitors in Germany for 2022 (E)
Table 39: Germany recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Type - Partially Autonomous and
Fully Autonomous - Independent Analysis of Annual Sales in US$
Million for the Years 2020 through 2027 and % CAGR

Table 40: Germany 7-Year Perspective for Autonomous Farm
Equipment by Type - Percentage Breakdown of Value Sales for
Partially Autonomous and Fully Autonomous for the Years 2021 &
2027

Table 41: Germany recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 42: Germany 7-Year Perspective for Autonomous Farm
Equipment by Product Type - Percentage Breakdown of Value Sales
for Tractors, Harvesters and Other Product Types for the Years
2021 & 2027

ITALY
Table 43: Italy recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Type - Partially Autonomous and
Fully Autonomous - Independent Analysis of Annual Sales in US$
Million for the Years 2020 through 2027 and % CAGR

Table 44: Italy 7-Year Perspective for Autonomous Farm
Equipment by Type - Percentage Breakdown of Value Sales for
Partially Autonomous and Fully Autonomous for the Years 2021 &
2027

Table 45: Italy recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 46: Italy 7-Year Perspective for Autonomous Farm
Equipment by Product Type - Percentage Breakdown of Value Sales
for Tractors, Harvesters and Other Product Types for the Years
2021 & 2027

UNITED KINGDOM
Autonomous Farm Equipment Market Presence - Strong/Active/
Niche/Trivial - Key Competitors in the United Kingdom for 2022
(E)
Table 47: UK recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Type - Partially Autonomous and
Fully Autonomous - Independent Analysis of Annual Sales in US$
Million for the Years 2020 through 2027 and % CAGR

Table 48: UK 7-Year Perspective for Autonomous Farm Equipment
by Type - Percentage Breakdown of Value Sales for Partially
Autonomous and Fully Autonomous for the Years 2021 & 2027

Table 49: UK recent Past, Current & Future Analysis for
Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 50: UK 7-Year Perspective for Autonomous Farm Equipment
by Product Type - Percentage Breakdown of Value Sales for
Tractors, Harvesters and Other Product Types for the Years 2021 &
2027

REST OF EUROPE
Table 51: Rest of Europe recent Past, Current & Future Analysis
for Autonomous Farm Equipment by Type - Partially Autonomous
and Fully Autonomous - Independent Analysis of Annual Sales in
US$ Million for the Years 2020 through 2027 and % CAGR

Table 52: Rest of Europe 7-Year Perspective for Autonomous Farm
Equipment by Type - Percentage Breakdown of Value Sales for
Partially Autonomous and Fully Autonomous for the Years 2021 &
2027

Table 53: Rest of Europe recent Past, Current & Future Analysis
for Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 54: Rest of Europe 7-Year Perspective for Autonomous Farm
Equipment by Product Type - Percentage Breakdown of Value Sales
for Tractors, Harvesters and Other Product Types for the Years
2021 & 2027

ASIA-PACIFIC
Autonomous Farm Equipment Market Presence - Strong/Active/
Niche/Trivial - Key Competitors in Asia-Pacific for 2022 (E)
India Jumps on the Autonomous Tractors Bandwagon
Government Initiatives to Benefit Market
Table 55: Asia-Pacific recent Past, Current & Future Analysis
for Autonomous Farm Equipment by Type - Partially Autonomous
and Fully Autonomous - Independent Analysis of Annual Sales in
US$ Million for the Years 2020 through 2027 and % CAGR

Table 56: Asia-Pacific 7-Year Perspective for Autonomous Farm
Equipment by Type - Percentage Breakdown of Value Sales for
Partially Autonomous and Fully Autonomous for the Years 2021 &
2027

Table 57: Asia-Pacific recent Past, Current & Future Analysis
for Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 58: Asia-Pacific 7-Year Perspective for Autonomous Farm
Equipment by Product Type - Percentage Breakdown of Value Sales
for Tractors, Harvesters and Other Product Types for the Years
2021 & 2027

REST OF WORLD
Table 59: Rest of World recent Past, Current & Future Analysis
for Autonomous Farm Equipment by Type - Partially Autonomous
and Fully Autonomous - Independent Analysis of Annual Sales in
US$ Million for the Years 2020 through 2027 and % CAGR

Table 60: Rest of World 7-Year Perspective for Autonomous Farm
Equipment by Type - Percentage Breakdown of Value Sales for
Partially Autonomous and Fully Autonomous for the Years 2021 &
2027

Table 61: Rest of World recent Past, Current & Future Analysis
for Autonomous Farm Equipment by Product Type - Tractors,
Harvesters and Other Product Types - Independent Analysis of
Annual Sales in US$ Million for the Years 2020 through 2027 and
% CAGR

Table 62: Rest of World 7-Year Perspective for Autonomous Farm
Equipment by Product Type - Percentage Breakdown of Value Sales
for Tractors, Harvesters and Other Product Types for the Years
2021 & 2027

IV. COMPETITION
Total Companies Profiled: 66
Read the full report: https://www.reportlinker.com/p05818534/?utm_source=GNW

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