Autonomous vehicles: Entertaining passengers may be the big opportunity for telecoms operators

While autonomous vehicles are gaining huge attention from all sectors, there is little assessment of what they mean for the telecoms sector. Based on a series of discussions with players in different roles in the value chain, and our own analysis, Analysys Mason believes that, while autonomous vehicles may have a transformative impact on society, their impact on the telecoms industry is likely to be modest, says Tom Rebbeck, research director, enterprise & IoT at Analysys Mason.

Autonomous cars will turn drivers into passengers, generating new demand for telecoms operators’ services

Autonomous vehicles are unlikely to rely on telecoms networks, despite the often-made association between 5G and autonomous driving: self-driving cars will depend more on on-board processing than the cloud. Real-time connectivity will be beneficial, but not essential.

Telecoms networks will be used for non-real-time updates to and from the vehicle (such as traffic information, mapping information and software updates), but bandwidth requirements for these services may be (relatively) low.

However, autonomous motoring will turn drivers into passengers, and potentially into consumers of video, gaming and audio content – all of which could generate new demand for telecoms operators’ services. The time frames for these developments this will be long: fully autonomous vehicles may not form the majority of vehicles until after 2030, depending on technology developments, regulation and consumer acceptance.

Figure 1 summarises the main opportunities for telecoms operators with autonomous cars.

Figure 1: Autonomous vehicle opportunities for operators

Autonomous cars do not need wide-area connectivity

Self-driving vehicles rely on information coming from their on-board sensors (for example, radar, lidar, optical) to navigate because cellular services cannot always be guaranteed to be reliable. Information from a wide-area connection will help supplement the on-board data, perhaps giving additional information about the actions and intentions of other vehicles, but the vehicle will never be dependent on that information.

This is essentially the way that experimental autonomous cars are working today – information from on-board sensors is combined with highly detailed (up to 10cm) maps. These maps can be updated, in non-real time, using a cellular connection (or via a Wi-Fi connection and fixed broadband).

Developments in ‘vehicle-to-everything’ (V2X) technology will not change this. The information available to the autonomous car will become richer, but will only act as a supplement to on-board systems. V2X could have other impacts though: unlike today’s experimental autonomous cars, which each act as an isolated unit, V2X technology could allow different vehicles to act in concert – for example allowing vehicle platooning or smoother traffic flows in cities.

Bandwidth requirements are hard to calculate, but may be (relatively) low

Intel created some interest by suggesting that autonomous cars will generate 4TB of data per day. However, this figure needs to be treated carefully. Based on the inputs provided by Intel, it seems this figure is based on a car driving for at least 15 hours a day – reasonable for the average self-driving Uber perhaps, but unlikely for a typical private car.

Intel’s 4TB figure must also be treated with caution because it is the amount of data that needs […]

The post Autonomous vehicles: Entertaining passengers may be the big opportunity for telecoms operators appeared first on IoT Now – How to run an IoT enabled business.

Blogs – IoT Now – How to run an IoT enabled business

Uber to buy thousands of Volvo autonomous drive compatible cars

Uber has signed a non-exclusive agreement with Volvo Cars to buy tens of thousands of self-driving compatible base vehicles between 2019 and 2021.

The Uber-Volvo Cars strategic partnership announced in August 2016 has been further enhanced with the new agreement, according to which Volvo Cars will supply an undisclosed number of XC90 premium SUVs to the ride sharing company. Engineers from both the companies worked together to formulate the car equipped with all required safety and core autonomous driving technologies that are required for the ride sharing company to add its own self-driving technology.

Håkan Samuelsson, president and chief executive of Volvo Cars, said: “The automotive industry is being disrupted by technology and Volvo Cars chooses to be an active part of that disruption. Our aim is to be the supplier of choice for AD ride-sharing service providers globally. Today’s agreement with Uber is a primary example of that strategic direction.”

Talking about the partnership, Uber’s head of auto alliances Jeff Miller said: “We’re thrilled to expand our partnership with Volvo. This new agreement puts us on a path towards mass produced self-driving vehicles at scale.”

Elsewhere, Lyft is looking to raise another $ 500 million, which the ride sharing company says is an extension of the recent $ 1 billion round led by CaptialG. As reported by TechCrunch, the additional $ 500 million funding will help Lyft to invest more capital into its passenger and driver products. The funding would come at a crucial time as Lyft is all ready to make its maiden move outside the US by entering the Canadian market in December.

iottechnews.com: Latest from the homepage

Autonomous vehicles: Three key use cases of advanced analytics shaping the industry

Driven by analytics, the culture of the automobile, including conventional wisdom about how it should be owned and driven is changing. Case in point, take the evolution of the autonomous vehicle. Already, the very notion of what a car is capable of is being radically rethought based on specific analytics use cases, and the definition of the ‘connected car’ is evolving daily.

Vehicles can now analyse information from drivers and passengers to provide insights into driving patterns, touch point preferences, digital service usage, and vehicle condition, in virtually real time. This data can be used for a variety of business-driven objectives, including new product development, preventive and predictive maintenance, optimised marketing, up selling, and making data available to third parties. It’s not only powering the vehicle itself, but completely reshaping the industry.

By using a myriad of sensors to inform decisions traditionally made by human operatives, analytics is completely reprogramming the fundamental areas of driving – perception, decision making and operational information. In this article, we discuss a few of the key analytics-driven use cases that we are likely to see in the future as this category, (ahem) accelerates.

The revolution of driverless vehicles

Of course, in the autonomous vehicle, the major aspect missing is the driver, traditionally the eyes and ears of the journey. Replicating the human functions is one of the major ways in which analytics is shaping the industry. Based on a series of sensors, the vehicle gathers data on nearby objects, like their size and rate of speed and categorises them based on how they are likely to behave. Combined with technology that is able to build a 3D map of the road, it helps it then to form a clear picture of its immediate surroundings.

Now the vehicle can see, but it requires analytics to react and progress accordingly taking into account the other means of transportation in the vicinity, for instance. By using data to understand perception, analytics is creating a larger connected network of vehicles that are able to communicate with each other. In making the technology more and more reliable, self-driving vehicles have the potential to eventually become safer than human drivers and replace those in the not so distant future. In fact, a little over one year ago, two self-driving buses were trialed on the public roads of Helsinki, Finland, alongside traffic and commuters. It was the first trials of its kind with the Easymile EZ-10 electric mini-buses, capable of carrying up to 12 people.

Artificial intelligence driving the innovation and decision making

In the autonomous vehicle, one of the major tasks of a machine learning algorithm is continuous rendering of environment and forecasting the changes that are possible to these surroundings. Indeed, the challenge facing autonomous means of transportation is not so much capturing the world around them, but making sense of it. For example, a car can tell when a pedestrian is ready to cross the street by observing behavior over and over again. Algorithms can sort through what is important, so that the vehicle will not need to push the brakes every time a small bird crosses its path.

That is not say we are about to become obsolete. For the foreseeable future, human judgement is still critical and we’re not at the stage of abandoning complex judgement calls to algorithms. While we are in the process of ‘handing over’ anything that can be automated with some intelligence, complex human judgement is still needed. As times goes on, Artificial (AI) ‘judgement’ will be improved but the balance is delicate – not least because of the clear and obvious concerns over safety.

How can we guarantee road safety?

Staying safe on the road is understandably one of the biggest focuses when it comes to automated means of transportation. A 2017 study by Deloitte found that three-quarters of Americans do not trust autonomous vehicles. Perhaps this is unsurprising as trust in new technology takes time – it took many years before people lost fear of being rocketed through the stratosphere at 500 mph in an aeroplane.

There can, and should, be no limit to the analytics being applied to every aspect of autonomous driving – from the manufacturers, to the technology companies, understanding each granular piece of information is critical. But, it is happening. Researchers at the Massachusetts Institute of Technology are asking people worldwide how they think a robot car should handle such life-or-death decisions. Its goal is not just for better algorithms and ethical tenets to guide autonomous vehicles, but to understand what it will take for society to accept the vehicles and use them.

Another big challenge is determining how long fully automated vehicles must be tested before they can be considered safe. They would need to drive hundreds of millions of miles to acquire enough data to demonstrate their safety in terms of deaths or injuries. That’s according to an April 2016 report from think tank RAND Corp. Although, only this month, a mere 18 months since that report was released, professor Amnon Shashua, Mobileye CEO and Intel senior vice president, announced the company has developed a mathematical formula that reportedly ensures that a "self-driving vehicle operates in a responsible manner and does not cause accidents for which it can be blamed."

Transforming transportation and the future

In many industries, such as retail, banking, aviation, and telecoms, companies have long used the data they gather from customers and their connected devices to improve products and services, develop new offerings, and market more effectively. The automotive industry has not had the frequent digital touch points to be able to do the same. The connected vehicle changes all that.

Data is transforming the way we think about transportation and advanced analytics has the potential to make driving more accessible and safe, by creating new insights to open up new opportunities . As advanced analytics and AI become the new paradigm in transportation, the winners will be those who best interpret the information to create responsive, learning, and connected vehicles capable of making autonomous vehicles as simple as getting from A to B.

iottechnews.com: Latest from the homepage

Connected Cars, Autonomous Vehicles, And The IoT

Part 2 of the “Future of Transportation and the Internet of Things” series

In my last blog, I talked about the simplicity of the electric engine compared to the internal combustion engine and how this changes everything. From climate to the structure of the auto industry to the way we store, manage, and distribute energy, electric cars are having tremendous impact.

But what I left out of that discussion was the Internet of Things.

Predictive

The fact is, most electric cars are connected cars – connected through the Internet of Things. This means that sensors in the car constantly communicate with mission control (the manufacturer), sending data on the status of components in real time.

By analyzing this data, especially in context of historical data, mission control can predict component failure before it happens. For electric vehicles – with engines that already need far less repair than traditional internal combustion engines – this only increases reliability.

But what’s more, IoT-connected cars also increase convenience. For example, after realizing component failure is imminent, your car could also trigger a work order at the dealership to resolve the issue – and ensure the needed replacement part is in stock when you roll in. And if the car is autonomous, it could drive itself to be repaired while you are at work and return ready to drive you home once the repair is completed. Speaking of autonomous….

Autonomous and safe

Connectedness is also what makes autonomous vehicles possible. And while some people may distrust driverless cars, the data shows that they’re safer than the self-driven sort – at least according to a report of the U.S. National Highway Traffic Safety Administration (NHTSA).

Back in May 2016, a Tesla Model S sedan in autopilot collided with a semi-truck in Florida, killing the driver (or passenger in this case?), 40-year-old Joshua Brown. The car, apparently, crashed into the truck, passed under the trailer, and kept driving for some distance – only coming to a stop after crashing through two fences and into a pole.

As a result of this incident, NHTSA conducted an investigation resulting in a report that largely exonerated Tesla. In fact, the report says that after the introduction of autosteer – a component of the autopilot system – Tesla’s crash rate dropped by 40%.

Self-learning

The accident in question happened when the semi-truck took a left-hand turn into oncoming traffic. The reason the Tesla did not detect such a large object in its path is because it could not distinguish the white color of the trailer from the bright, white Florida sky in the background.

Reportedly, Tesla has since analyzed the crash data from this accident, identified the problem, and made fixes to the operating system on which its fleet operates. Perhaps it’s premature to declare the problem solved – but the idea at play here is an interesting one when considering the potential for connected cars and the IoT.

What this scenario shows is a learning platform in action. Because all of its cars are connected on a single platform, Tesla has access to a tremendous amount of driver data that it can analyze to continuously improve product safety. I don’t know exactly how the analysis proceeded in this particular case, but one can certainly envision the use of machine learning technology to continuously analyze patterns and introduce safety improvements on the fly, making the self-learning platform a reality.

Disruptive

A future in which autonomous vehicles are not only viable but safer than self-driven cars will result in disruptions beyond those I’ve indicated for electric engines.

Take the insurance industry, for example. With fewer accidents comes lower risk – leading to lower insurance premiums. And in a future where most cars on the road are autonomous – connected and controlled via IoT – the insurable entity will likely shift from the driver (who is now a passenger) to the operator of the network (presumably the manufacturer). Certainly, if you decide you wish to drive your car yourself, your insurance will be significantly more expensive than the insurance for an autonomous vehicle.

Of course, if autonomous cars can get where they’re going without a driver, why even bother owning a car? Why not just call up the ride when you need it, Uber style?

One result would be optimal asset utilization, where cars that are far less likely to break down can be used on an almost 24×7 basis by spreading usage across individuals. This would mean we’d need far fewer cars on the road, which would alleviate congestion. It would also hit the auto industry with dramatically lower sales volume.

And with fewer cars on the road – cars that are in use almost all the time – we’d have less use for parking. This would have tremendous impact on an industry that generates approximately $ 20 billion annually.

Beyond industry disruption, less need for parking would open up tremendous urban space in the form of unused lots and garages. Maybe this would mean more populous cities with room to build for more people to live more comfortably without traffic congestion. Or how about using some of the space for indoor vertical farming using hydroponics technology and LED lights to grow more food and feed more people? Of course, this is already happening. But that’s a blog for another time.

To meet the market’s expectations for increasingly fast, responsive, and personalized service, speed of business will be everything. Find out how innovative processes can enable your business to remain successful in this evolving landscape. Learn more and download the IDC paper “Realizing IoT’s Value – Connecting Things to People and Processes.”


Internet of Things – Digitalist Magazine

Walmart testing autonomous shelf-scanning robots

Walmart testing autonomous shelf-scanning robots

Following a successful initial trial, multinational retailer Walmart is rolling out its autonomous robots to perform shelf-scanning duties at a further 50 stores.

The scale of repetitive, predictable processes in retail stores, warehouses and supply chain operations makes them perfect candidates for automation. Walmart has been quick off the mark in recognizing this, with its use of inventory checking drones in distribution centres, and its initial testing of robots in Arkansas, Pennsylvania and California. It’s now looking to expand its rollout to stores across the US.

Walmart is the world’s largest company by revenue (approximately $ 480 billion, according to the Fortune Global 500 list last year), as well as the largest private employer, with 2.3 million employees. As such, there are potentially billions of dollars in efficiency savings to be had by increasing automation.

Read more: Walmart partners with August Home to deliver direct to fridges

The Walmart scanning robot

The retailer’s monolithic-looking robot sits at its charging station until called upon. It then performs several scanning duties as it negotiates its way around the store – including recording out-of-stock items, incorrect prices and erroneous or missing labels. Once the robot has completed its task, it forwards its findings to Walmart employees, so that they can respond to the issues the robot highlights.

The robots were designed and produced by California-based Bossa Nova Robotics, with technology developed over the last five years.

Walmart is keen to emphasize the robots’ benefits to both the consumer and the company’s employees. The machine’s regular scanning duties mean that stock levels are more accurately maintained, broadening the range of products available to online customers. It also helps personal shoppers fulfil orders.

John Crecelius, Wal-Mart US vice president of central operations spoke to the Arkansas Democrat-Gazette about the initiative. “If you think about trying to go through a facility with all these different [items] and figure out if your prices are accurate, it can be very time-consuming,” Crecelius points out. “Then to try to figure out what to do about it. Imagine how much time you’ve lost in doing all that.”

Read more: Robot shopping carts make Walmart debut

Employees to miss out?

While the potential time savings are clear to see, with automation comes the fear that blue and pink-collar workers will lose their jobs. Last year, Wal-Mart installed cash recycler machines in its stores and invoicing operations, leading to the removal of around 7,000 positions in-store.

Walmart maintains that this will not be the case in this instance. By having its robot perform the repetitive but vital shelf-scanning duties, store associates are freed-up to better assist customers and sell merchandise.

The programme will be informed by feedback from store associates and customers, in the belief that this will ensure a solution that’s beneficial for all parties. There are also big data benefits to be gained. The information collected can be further analysed to identify opportunities and issues in each store or region.

Read more: MIT researchers develop drone inventory system, RFly

Walmart and IoT

Walmart’s August announcement on its partnership with Google to offer personalised voice shopping – a platform that went live earlier this month – is further evidence of the company’s predilection for innovation. By linking their Walmart account to Google Express, customers can receive recommendations based on their purchase history, both in-store and online. Google’s recently released Home Mini and Amazon’s new Echo devices mean hands-free experiences, including shopping, are more accessible than ever.

Walmart’s use of drones, robotics and other innovations reveals a clear strategy of using the latest technology to streamline retail and purchasing processes. I have no doubt that their introduction will introduce efficiency improvements, but this enthusiasm is tempered with concern for the security of the world’s largest workforce.

The post Walmart testing autonomous shelf-scanning robots appeared first on Internet of Business.

Internet of Business