Drivers will soon have an extra set of eyes on the road, courtesy of Google.

Google’s navigation app, Google Maps, is starting to roll out speed limit and speed trap features. 

With the speed limit feature, drivers using Google Maps will be shown the posted speed limit of the road they’re driving on in the lower left side of the app. Speed traps are designated with a small camera icon and shown on the visible area of the map. The Maps app provides an audio warning for drivers when they are approaching a speed trap.

Users have had the ability to report speed traps in Google Maps since late last year. However, until now, the app wasn’t yet making the reported speed traps visible for users in maps. 

Google Maps users have long wondered when such features were going to make their debut on the service. Crowdsource-powered navigation app, Waze, grew in popularity for providing this sort of detailed information on the road. Google acquired Waze back in 2013 for around $1 billion. 

Now, nearly five and a half years later, some of the Waze features are finally making their way to Google Maps.

Several companies demonstrated C-V2X (cellular vehicle-to-everything)communications technology developed by Qualcomm at CES 2019. But Ford may be the most aggressive in bringing C-V2X to market. The Detroit automaker will add it to every new car and truck sold in the United States by 2022, Don Butler, executive director for Ford connected vehicle platform and product, wrote in a blog post.

C-V2X is the latest incarnation of so-called vehicle-to-everything (V2X), a communications technology that allows cars to “talk” to each other and infrastructure. The claimed advantage of this technology is that it can warn a driver of things beyond his or her line of sight. If a car is stopped at an intersection with poor visibility, it could, for example, pick up signals from other V2X-equipped cars or sensors mounted on nearby buildings to tell the driver if it’s okay to go.

Vehicles could also communicate with stoplights, telling drivers when a light is about to change. Audi already offers this in the form of its Traffic Light Information system. The system gives a countdown when a light is about to turn green, but it only works in a handful of cities (Audi also offers a built-in toll transponder that relies on V2X tech). Aptiv has placed sensors on traffic lights in Las Vegas to guide its self-driving cars, even when they’re onboard cameras don’t have a direct line of sight to the light.

Ford could take things even further, Butler wrote. C-V2X could be integrated with driver aids, like those in Ford’s recently introduced Co-Pilot360 suite. Or it could be added to self-driving cars. Emergency vehicles could be equipped with C-V2X transmitters, allowing cars to detect their presence and move out of the way.

The difference between the C-V2X tech embraced by Ford and previous systems is that it’s based on 5G. All other V2X systems to-date have used a competing setup called Dedicated Short-Range Communications (DSRC). But that means Ford will have to rely on the smooth rollout of 5G. Even the deployment of DSRC-based V2X vehicles and infrastructure has been slow, and DSRC is based on a more familiar technology derived from Wi-Fi.

“A conducive regulatory environment must be in place for C-V2X to be deployed, which is why we are working just as much with industry and government organizations to create such a technology-neutral environment,” Butler wrote in his blog post. He also told Bloombergthat he hopes other automakers will adopt C-V2X alongside Ford. He added that C-V2X is a simpler solution because telecom companies are already spending billions on 5G cell towers and antennas, while DSRC would require a separate government investment.
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Despite the potential hurdles, Qualcomm expects C-V2X and other related technologies to become a significant part of its business. The company believes that, within five years, 75% of cars will have some form of connectivity. A critical mass of vehicles will be needed to realize the technology’s full benefit since cars that aren’t equipped with C-V2X or similar systems can’t communicate with each other. The more vehicles on the network, the more effective it is.

British scientists have developed a self-contained and tamper-proof "quantum compass" that doesn't rely on GPS signals to provide a highly accurate measure of where it is in the world.

The compass is a quantum accelerometer that is capable of measuring tiny shifts in supercooled atoms and so calculate how far and how fast the device has moved. Having a device like the quantum compass aboard a ship, the captain knows exactly where his ship is without having to rely on orbiting satellites.

The system has been designed by quantum physicistsat Imperial College, London – who developed a laser system for cooling atoms down and at photonics and quantum technology specialist firm M Squared– which developed another laser system to act as an “optical ruler.” Their work has been funded by the UK's Ministry of Defense.

Although GPS satellites are a modern marvel and are used by just about everyone to identify their precise location, the fact is the system is not perfect.

A phone's GPS is accurate to roughly 15 feet, although military GPS devices can be accurate to centimeters. Then there is the fact that tall buildings will often throw a signal off and signals can be impaired by any large, dense object.

But that's not why the Ministry of Defense is interested in a quantum compass: its concern is that the GPS system is vulnerable to attack or deliberate disruption. A GPS signal could be spoofed or blocked for instance. When you're thinking about nuclear submarines, it's usually best to consider the worst.

“Pirates are now sophisticated enough to cause disruptions to ships’ GPS systems and lure them to rocks or take over and board them,” said Graeme Malcolm, the CEO of M Squared.

“They can be an even bigger issue in areas of defense and security, where the resilience and security of cities and countries are impacted. This new device is an absolute reference that goes down to the atomic level.”

Accuracy. This is not the first time that accelerometers have been used for navigation, but the reality is that such systems are not sufficiently accurate, meaning that as time goes on the system becomes increasingly out of whack and requires recalibration.

Not so with the quantum compass, according to its makers. Lasers are used to supercool atoms, and then another laser is used to act as "optical ruler" measuring how far those atoms have moved. At extremely low temperatures, atoms behave in a ‘quantum’ way, acting like both matter and waves.

Here's how Dr. Joseph Cotter, from the Centre for Cold Matter at Imperial College, explained it: "When the atoms are ultra-cold, we have to use quantum mechanics to describe how they move, and this allows us to make what we call an atom interferometer."

As the atoms move, their wave properties are affected by the acceleration of the vehicle. The optical ruler can measure these minute changes very accurately and then with a few relatively simple equations it is possible to figure out exactly where you are.

Size. But this is not something you're going to find in your smartphone: the prototype system is about three-feet wide and high, and it is incredibly expensive.
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Plus, it can currently only measure in one plane. The scientists say they will soon be able to take measurements in three planes – making it an entirely independent super compass that can tell you where exactly it is at any point.

Every election season, Uber and Lyft jockey for the position of the most civic-minded ride-hailing company by offering various promotions, like free and discounted rides to polling places. This year, both companies are trying something new. On November 6th, Uber will add a new button in its app that will help people find and book rides to their polling place. It’s an extra step that Uber says it hopes will help get out the vote. Lyft also promises something similar. 

Uber. The poll locator will be available to anyone who lives in the US, but it’s targeted at suburban or exurban voters who tend to live outside of walking distance of their polling location. There are a variety of websites where voters can go to find their polling information but pairing that with a transportation service could help bridge the gap between the intention to vote and going to the polls. 

Lyft. The San Francisco-based ride service is also aiming to help its customers find their polling location. The company said in a recent announcement that it would release a “product integration to help passengers find their polling location.” It didn’t, however, specify what that integration would look like.

Surveys indicate that voter enthusiasm is at an all-time high this year, but if history is any indicator, most eligible voters will stay home. Slightly more than one-third of eligible voters turned out across the country in the last midterm elections.

Uber is hoping it can make a small contribution to turnout. “Using our technology and resources, we can help make it easier for every Uber rider in the US to get to their polling place at the push of a button,” Uber CEO Dara Khosrowshahi said in a blog post.

The ride-hail company will also partner with two GOTV organizations, #VoteTogether and Democracy Works, to provide free rides to voters in specific communities. Uber will also help register riders and drivers to vote by sharing voter registration information through the app. An email went out to all Uber drivers this week encouraging them to visit one of Uber’s 125 Greenlight Hubs where they can register to vote. 
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In August, Lyft saidit planned to give away 50 percent off promo codes with GOTV partners to encourage voter turnout. Neither Uber nor Lyft would say how many free and discounted rides they plan on distributing.

Google frequently touts that the “next billion users” will come from developing nations with different focuses and needs. To that end, the company has developed several optimized services, with one being a consistent and straightforward addressing system that works globally.

Google created “Plus Codes”for addresses that are not easily located through conventional descriptors. First introduced in February 2018, Plus Codes allow users to locate almost any spot on the planet. What many people in traditional civilizations fail to realize is that, according to a World Bank estimate, half of the world’s urban population lives on unnamed streets.

Notably, this open source solution composed of 10 characters works globally and can be incorporated by other products and platforms for free, with a developer page available here.

It works offline and in print when overlaid as a grid on existing maps. Places that are close together share similar plus codes, while the system is identifiable by the “+” symbol in every address.

This system is based on dividing the geographical surface of the Earth into tiny ‘tiled areas,’ attributing a unique code to each of them. This code comprises a ‘6-character + City’ format that can be generated, shared and searched by anyone – all that’s needed is Google Maps on a smartphone.

                   •   The first four characters are the area code, describing a region
                        of about 100 x 100 kilometers.

                   •  The last six characters are the local code, describing the neighborhood
                       and the building, an area of approximately 14 x 14 meters – about the
                       size of one half of a basketball court.

The area code is not needed when navigating within a town, while another optional character can be appended to provide additional accuracy down to a 3 x 3-meter region. Plus codes for any location can be found with this tool.

Potholes are a nuisance pretty much everywhere, and the methods traditionally used to keep track of them aren't efficient. Whether it be driving around to visually inspect roads or watching videos of streets to spot and prioritize the repair of pavement damage, strategies typically used by towns and cities require a fair amount of time and labor. But AI could make that process a bit easier and quite a bit faster. One group exploring the use of AI to support the municipal officials in charge of road maintenance has been developing a software system that could not only aid in road repairs but also bridge and building maintenance as well.

The group, led by John Zelek at the University of Waterloo, has been using road images sourced from Google Street View to develop its system, which automatically analyzes photos of roads to spot potholes, cracks, and other damage. In theory, municipal officials could use the software to analyze images taken via vehicle-mounted phone cameras when employees are driving as part of their day-to-day work. "If governments have that information, they can better plan when to repair a particular road and do it at a lower cost," Zelek said in a statement. "Essentially, it could mean lower taxes for residents."
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The researchers are also looking to apply the system to drone images taken of bridges, buildings, and other infrastructure as well as images of construction projects. "If the parts of a new building aren't coming together properly, it would be very beneficial to flag problems before the next 30 stories have been put up," said Zelek.

Sometime in early 2019, the self-driving e.GO Mover bus will hit the streets in Germany. The electrically powered vehicle can transport up to ten people, with a battery that lasts for ten hours or so.

And, it's powered by the cloud – the Microsoft Azure cloud platform, to be exact.

While autonomous vehicles powered by the cloud are not new, e.GO Mover highlights how artificial intelligence (AI), the Internet of Things (IoT), and edge computing projects are increasingly coming together to power smart cities. Amazon Web Services (AWS), for example, boasts customers including DriveAI, nuTonomy, TuSimple, and Mapillary—all of which work on autonomous vehicle systems.

Google Cloud Platform also lists best practicesfor building a connected vehicle solution for autonomous driving and vehicle-to-vehicle (V2V) communication on its platform as well.

The growth of such a trend is enabled by the availability of powerful AI tools on these cloud platforms. Machine learning tools are crucial to the development of such autonomous systems, and public cloud giants make them much more accessible. This helps the growth of IoT projects in smart cities as well.

Of course, the cloud on its own will not be able to drive the digital transformation of smart cities fully. Improved latency through next-generation 5G connectivity will also play a role, as autonomous vehicles and IoT devices will be able to send and receive critical data while operating more readily.

But, what happens when connectivity is spotty? That's when edge computing comes into play. Edge computing occurs when data is processed at the same location where it is collected. This means that autonomous vehicles or IoT devices will need some compute power on-board to be able to analyze the data they're receiving, without having to send it over a network to be processed and sent back.

Edge computing is especially crucial in autonomous vehicles, as the potential danger posed by poor operations is immense.
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As more and more cities begin to embrace digital transformation, these three technologies will play an integral role in their future. And, as city officials start to rely more and more on these tools, we may see network operators, public cloud vendors, and IoT providers operating similarly to the way our utility providers operate today.

Passengers may soon have a more hands-free and hassle-free airport experience thanks to advances in biometric technology.

Sydney Airport in Australia has begun the initial phase of testing facial recognition technologyon select international flights for Qantas, the country’s flag carrier. When complete, the technology would allow flyers to complete “most stages of their airport journey using their face as their access identification.”

The first phase will test the technology at four “key steps” in the passenger journey, including automated check-in, bag drop, lounge access, and boarding. The airport said additional steps for future trials could include mobile check-in and automated border processing.

“In the future, there will be no more juggling passports and bags at check-in and digging through pockets or smartphones to show your boarding pass – your face will be your passport, and your boarding pass at every step of the process,” Sydney Airport CEO Geoff Culbert said in a statement.

The airport, which has 43 million passengers pass through annually, is the primary hub for Qantas. The airline said consent is sought from all customers participating in the trial and would follow the “strictest privacy standards” and comply with all “relevant legislation.”

“There is an increasing need for airlines and airports to offer faster and more convenient airport experiences, and we’re excited to see what results the trial produces,” Qantas Chief Customer Officer Vanessa Hudson said in a statement.
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The border processing procedures of the Australian government will not change, with travelers still required to undergo current border processes, the airport said.

A new study of ride-hailing services in Los Angeles suggests that apps like Uber and Lyft may be dramatically increasing transportation access for minority groups and in lower-income neighborhoods. The impact of this improvement is potentially outsized because those populations have been so poorly served by traditional taxis and are less likely to own private vehicles. Ride-hailing services, then, may be dramatically increasing their overall mobility, particularly in car-centric cities such as Los Angeles.

The new research, conducted by UCLA Ph.D. candidate Anne E. Brown and described by CityLab, used data from Lyft to find that the company’s drivers served 99.8% of Los Angeles and that users living in low-income areas made more trips per capita than those in middle- and high-income areas. The study also found that lower car ownership, which is correlated with lower income and minority status, also correlated to increased Lyft use.

That suggests that what is a situational convenience for higher-income riders is a lifeline to those without cars. Time and again, research has shown that public transport in American cities fails to give more impoverished residents access to jobs,both disadvantaging those individuals, and hampering city economies. While ride-hailing services are generally more expensive for riders than public transit, they’re more convenient and reliable than most bus services, and certainly better than nothing at all.

The discrimination faced by poor and minority communities in the pre-Uber era had at least three elements. Studies and surveys across several major cities have consistently shown that, when hailed on the street, conventional taxis are less likelyto stop for black passengersin particular. Taxis have also long been harder to get in neighborhoods populated by poorer residents, including those in New York City’s outer boroughs.
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Though Brown’s findings are limited to Los Angeles, they are likely to influence ongoing fights over regulation between ride-hailing companies and cities.

A new lidar sensor could equip thousands of driverless cars with the sensing abilities required to drive at high speeds on the open road.

Lidar has become the primary way most driverless cars sense the world around them, bouncing laser light off nearby objects to create 3-D maps of their surroundings.

For years, the industry leader in lidar has been Velodyne, which builds some of the most expensive, ultra-high-resolution sensors available. But the rapid growth of self-driving vehicle research prompted other firms to start making them too – among them, a startup called Luminar, which was set up by Stanford drop-out Austin Russell and came out of stealth last year.

Luminar’s technology is different to other lidar systems. It uses a longer wavelength of light to operate at higher powers, allowing it to see darker objects over longer distances. It’s also able to zoom in on areas of specific interest.

But its sensors, which use a mechanical mirror system and expensive indium gallium arsenide semiconductors, were difficult and pricey to produce. Early units cost at least tens of thousands of dollars and required an entire day of human labor to assemble.

Over the last year, Russell, who was one of MIT Technology Review’s 35 Innovators Under 35 in 2017, says the firm has taken steps to change that. It acquired a chip design firm called Black Forest Engineering, hired consumer electronics experts, and set up its manufacturing complex in Orlando – all with the aim of building its sensor at commercial scale.

As a result, Russell says that the latest version of the sensor is approaching being auto-grade, meaning it should be ready for extreme temperatures, inclement weather, and other adverse conditions a production car might be exposed to (though it’s yet to be certified as such). The careful redesign of its laser detector chip, meanwhile, has cut its cost from tens of thousands of dollars to just $3, and automation means the sensors can now be built in eight minutes.

All of that means Luminar reckons it can offer a set of sensors for “a few thousand” dollars, Russell says. At the same time, it’s also boosted the specs, so the sensor can detect objects that are 250 meters off—enough for 7 seconds of reaction time at 75mph.

Ingmar Posner, an associate professor of information engineering at the University of Oxford and founder of the university’s autonomous-driving spinoff Oxbotica, says that the specifications and price point of the sensor “sound great.” But he also points out that the price will need to fall further if the sensors are to be used in affordable consumer vehicles.

That could yet happen. Russell points out that the sensor cost is related to the scale of production, and by the end of the year Luminar plans to be building 5,000 of its sensors every quarter. That’s a lot – more than the 10,000 sensors that competitor Velodyne planned to build last year – and would give it enough, Russell claims, to equip every autonomous test car on the roads by the end of this year.

The major hurdle to that kind of market dominance is convincing other research groups and automakers to switch from their existing sensors – something that would require rewriting control software and re-mapping entire cities so cars can navigate using the new sensors. Russell likens the situation to “ripping off a Band-Aid,” because it will need to happen at some point as carmakers switch to using auto-grade, rather than experimental, sensors.
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What remains to be seen for Luminar, though, is just how soon that Band-Aid gets pulled.