Ford Is Working With MIT, Stanford To Build “Common Sense” Into Self-Driving Cars

Automakers are building research vehicles that
can take in vast amounts of data about their
surroundings in a split second.  Now it's up to data
scientists to figure how cars can use that information.

As reported by GigaOM:  Ford Motor Company is teaming up with the Massachusetts Institute of Technology and Stanford University to research the future brains of its autonomous cars. Projects like Ford’s research vehicles are putting the sensors and computing power into cars that would allow them to read and analyze their surroundings, but these two universities are developing the technology that will allow them to make driving decisions from that data.

“Our goal is to provide the vehicle with common sense,” Ford Research global manager for driver assistance and active safety Greg Stevens said in a statement. “Drivers are good at using the cues around them to predict what will happen next, and they know that what you can’t see is often as important as what you can see. Our goal in working with MIT and Stanford is to bring a similar type of intuition to the vehicle.”

In December, Ford unveiled its latest research vehicle, a Ford Fusion Hybrid equipped with Lidar (laser-radar) rigs, cameras and other sensor arrays, all intended to generate a real-time representation of the world around the car. Such a car can “see” in all directions, allowing it not only to take in far more stimuli than even the most alert driver, but also to react to that information far more quickly. That’s where Stanford and MIT come in.

The Ford Fusion research vehicle from Lidar’s point of view

MIT is developing algorithms that will allow an autonomous driving system to predict the future locations of cars, pedestrians and other obstacles. It’s not good enough for a car to merely sense the location of nearby vehicles when it switches lanes or swerves to avoid an accident. It has to know where those vehicles will be a split-second later. Otherwise the car will avoid one accident only to cause another.

That means not only measuring other vehicles’ current speed and trajectory but anticipating how their drivers – or their autonomous vehicle systems – will react to the situation. Basically MIT is trying to create a vehicle brain smart enough to assess risks and outcomes and navigate its course accordingly.

Stanford is doing something a bit different. It’s trying to extend the sensory field of the car by helping it see around obstacles so it can react to dangers the driver can’t immediately see. Stanford and Ford didn’t offer any specifics on just how they would accomplish that feat, by my bet is it has to do with Ford and the automotive industry’s work on inter-vehicle networking.

Future autonomous cars won’t just be able to sense their surroundings, they’ll be able to communicate with other vehicles using a secure form of Wi-Fi. For instance, Australian startup Cohda Wireless is developing to vehicle-to-vehicle networking technology that would allow two cars to let each other know they’re approaching one another at a blind intersection.

Ford and other major automakers are working with the University of Michigan and the National Highway Traffic Safety Administration to build vehicle-to-infrastructure grids that would allow cars to tap into highway sensors, giving them a kind of omniscient view of the overall road. With such technology other cars could reveal their intentions before they even take action, making other connected vehicles much more responsive. They could also share their sensor data, so even if only one of the cars far ahead of you is connected to the vehicle grid, that lone vehicle could still tell you what the other cars around it are doing.

While every major automaker is working on autonomous driving technology, Ford has been particularly aggressive. In a recent interview, executive chairman Bill Ford told me how the automaker is trying to use connected vehicle technology to propel the company into a new golden age of automotive innovation.

What Spectrum Crisis? First Airwave Auction In 6 Years Kicks Off With Little Fanfare

The U.S. mobile industry claims it's desperate for new spectrum.
But when the FCC opened it's first mobile broadband airwave
auction in a half a decade, not a single major carrier participated.

As reported by GigaOM:  For the first time in half a decade, the Federal Communications Commission on Wednesday opened up bidding on a new spectrum auction, releasing new airwaves for 3G or 4G data services to potential buyers. But you would hardly know it, judging from the lack of interest across the mobile industry.

Not a single one of the four nationwide providers is participating in what is now known as Auction 96, which is distributing 10 MHz of frequencies in the 1900 MHz PCS band nationwide. That’s not exactly the kind of behavior you’d expect from an industry that insists it faces a spectrum crisis of the direst order.

Granted 10 MHz isn’t much in the grand scheme of things. It’s enough to add incremental 3G capacity or deploy an LTE network half the size of what most major operators have in the field today. But bandwidth is bandwidth. Yet the major carriers have decided to take a pass on these airwaves, looking ahead to the FCC’s big incentive auction next year. If done right, that auction could open large chunks of frequencies in the much more desirable 600 MHz band.

So who is participating in Auction 96? Dish Network is probably the one name you’ll recognize, but a lot of smaller carriers are also submitting bids in hopes to add to their regional holdings. In three rounds, bids now total $221 million. As you would expect, licenses in the big cities are attracting the most interest. The New York City license alone has attracted one quarter of all bids so far, followed by Los Angeles and Chicago.

The auction is just getting started, and it doesn’t run at quite the fast pace as estate sale. The FCC holds three bidding rounds each day until there are no more bids. This auction certainly won’t be the two-month-long process we saw in 2008 for the highly contested 700 MHz airwaves. We’ll probably see this auction conclude in a few weeks if not less.

Battery Modification May Add 27 Cumulative Years Of Life To GPS Satellite Fleet

The GPS IIR/M satellites have been launched between 1997 and
2009.

As reported by NextGov: The Air Force Space Command’s Space and Missile Systems Center and a team of contractors have extended the operational life of 19 GPS satellites in orbit by re-configuring their battery chargers.

Lockheed Martin launched the GPS IIR/M satellites between 1997 and 2009, and the fleet accounts for more than half the 36 GPS birds on orbit with batteries, the “the primary life-limiting component when GPS IIR/IIR-M vehicles are past their design life,” SMC said.

Aerospace Corp., a federally funded research and development center with extensive GPS expertise,  Lockheed and SMC determined that reducing the charge rates during solstice season would add an average of one to two years of life per space vehicle.

Last week, the 2nd Space Operations Squadron, Schriever Air Force Base, Colo., completed the battery charge modification, which will extend the life of each of the GPS IIR/IIM satellites by one to two years, more than 27 years of cumulative life across fleet, SMC said.

The changes represent a savings of hundreds of millions of dollars for the US government.

How AI Turns Traffic Lights Into Intelligent Agents

As reported by ReadWrite: Today’s new cars are loaded with sensors and powerful computer processors. That’s the high-tech pathway to turning our vehicles into super-efficient, semi-autonomous—or even self-driving—"transportation devices."

Unfortunately, the roads these clever mobility machines drive on are all too often, well, dumb. You experience the pain of this problem every time you senselessly wait for an extra couple minutes at a red light, when there are no other cars in sight from any direction.

Samah el-Tantawy, a recently minted PhD of Engineering from the University of Toronto, wants to change that.

Inspired by research from her advisor and director of the Toronto Intelligent Transportation Systems Centre, Professor Baher Abdulhai, el-Tantawy devised a system that uses artificial intelligence and game theory that, in a simulated environment, shaved 40% of the time off an average wait at an intersection. She said that could mean 12.5 fewer minutes stuck in your car, if you pass through about 30 intersections on your commute.

Can We Talk?

According to el-Tantawy, many of today’s traffic lights at intersections operate based on pre-programmed repeated cycles that run with little or no input from fluctuations in traffic. Yes, there are sensors in pavements along major arteries, but those inputs into centralized systems might only be able to extend a green light for a few seconds. Like other centralized disconnected top-down systems, there are inherent limitations.

Instead, el-Tantawy’s system—dubbed MARLIN for Multi-agent Reinforcement Learning for Integrated Network (of Adaptive Traffic Signal Controllers)—uses video cameras, other vehicle data inputs (if available), processing power, and routers to analyze how many drivers are zipping through the intersection and how many are simmering with road rage for wasting countless minutes at a red light. With MARLIN, cameras are aimed at all four approaches, and the system is distributed throughout a region rather than just on main streets.

“Our approach is decentralized, where the intelligence or math to assign the greens is done on the fly at each intersection,” she told me.  “The brain sits at each intersection, and calculates the best timing to minimize the number of cars approaching and waiting, and it coordinates those decisions with other lights at other intersections.”

The Shortest Wait Wins

El-Tantawy said no amount of math can perfectly model every situation. There are too many variables. The solution? “Each intersection is connected to the neighboring or adjacent intersection, sending and receiving information about the waiting vehicles,” she said.  Then, “reinforcement learning” comes into play.

Like a child learning to walk by making minute adjustments, each traffic light—or “agent,” as el-Tantawy calls them—makes a decision every second about the best way to keep motorists and pedestrians waiting for as short a period as possible.


“The agents learn, until they converge, with each one getting the best response action to achieve its goals, without negatively affecting the others. We use multi-agent reinforcement learning,” she said. “And it cascades throughout the system. The decisions by agents affect each other, so it’s a game.”

The system has to be simulated in a test environment before being placed in the street, where the learning can continue in real-world conditions. So far, MARLIN has only been used in a test environment—but with great results. That encouraged el-Tantawy and Professor Abdulhai to recently form a start-up traffic tech company to commercialize the system, and get it on as many streets as possible.

It's easy to see how this AI-powered traffic light system could be a major boost to a region's productivity, justifying the anticipated cost of about $20,000 to $40,000 per intersection.

el-Tantawy said her start-up will soon sign up its first municipality to run a field test, but wasn’t ready yet to disclose the location. I hope it’s in my neighborhood.

Big Data: The Power To Decide

What's the point of all that data, anyway?  It's to make
decisions.

As reported by MIT Technology Review: Back in 1956, an engineer and a mathematician, William Fair and Earl Isaac, pooled $800 to start a company. Their idea: a score to handicap whether a borrower would repay a loan.

It was all done with pen and paper. Income, gender, and occupation produced numbers that amounted to a prediction about a person’s behavior. By the 1980s the three-digit scores were calculated on computers and instead took account of a person’s actual credit history. Today, Fair Isaac Corp., or FICO, generates about 10 billion credit scores annually, calculating 50 times a year for many Americans.

This machinery hums in the background of our financial lives, so it’s easy to forget that the choice of whether to lend used to be made by a bank manager who knew a man by his handshake. Fair and Isaac understood that all this could change, and that their company didn’t merely sell numbers. “We sell a radically different way of making decisions that flies in the face of tradition,” Fair once said.

This anecdote suggests a way of understanding the era of “big data”—terabytes of information from sensors or social networks, new computer architectures, and clever software. But even supercharged data needs a job to do, and that job is always about a decision.

In this business report, MIT Technology Review explores a big question: how are data and the analytical tools to manipulate it changing decision making today? On Nasdaq, trading bots exchange a billion shares a day. Online, advertisers bid on hundreds of thousands of keywords a minute, in deals greased by heuristic solutions and optimization models rather than two-martini lunches. The number of variables and the speed and volume of transactions are just too much for human decision makers.

When there’s a person in the loop, technology takes a softer approach (see “Software That Augments Human Thinking”). Think of recommendation engines on the Web that suggest products to buy or friends to catch up with. This works because Internet companies maintain statistical models of each of us, our likes and habits, and use them to decide what we see. In this report, we check in with LinkedIn, which maintains the world’s largest database of résumés—more than 200 million of them. One of its newest offerings is University Pages, which crunches résumé data to offer students predictions about where they’ll end up working depending on what college they go to (see “LinkedIn Offers College Choices by the Numbers”).

These smart systems, and their impact, are prosaic next to what’s planned. Take IBM. The company is pouring $1 billion into its Watson computer system, the one that answered questions correctly on the game show Jeopardy! IBM now imagines computers that can carry on intelligent phone calls with customers, or provide expert recommendations after digesting doctors’ notes. IBM wants to provide “cognitive services”—computers that think, or seem to (see “Facing Doubters, IBM Expands Plans for Watson”).

Andrew Jennings, chief analytics officer for FICO, says automating human decisions is only half the story.

Credit scores had another major impact. They gave lenders a new way to measure the state of their portfolios—and to adjust them by balancing riskier loan recipients with safer ones. Now, as other industries get exposed to predictive data, their approach to business strategy is changing, too. In this report, we look at one technique that’s spreading on the Web, called A/B testing. It’s a simple tactic—put up two versions of a Web page and see which one performs better (see “Seeking Edge, Websites Turn to Experiments” and “Startups Embrace a Way to Fail Fast”).


Until recently, such optimization was practiced only by the largest Internet companies. Now, nearly any website can do it. Jennings calls this phenomenon “systematic experimentation” and says it will be a feature of the smartest companies. They will have teams constantly probing the world, trying to learn its shifting rules and deciding on strategies to adapt. “Winners and losers in analytic battles will not be determined simply by which organization has access to more data or which organization has more money,” Jennings has said.

Of course, there’s danger in letting the data decide too much. In this report, Duncan Watts, a Microsoft researcher specializing in social networks, outlines an approach to decision making that avoids the dangers of gut instinct as well as the pitfalls of slavishly obeying data. In short, Watts argues, businesses need to adopt the scientific method (see “Scientific Thinking in Business”).


To do that, they have been hiring a highly trained breed of business skeptics called data scientists. These are the people who create the databases, build the models, reveal the trends, and, increasingly, author the products. And their influence is growing in business. This could be why data science has been called “the sexiest job of the 21st century.” It’s not because mathematics or spreadsheets are particularly attractive. It’s because making decisions is powerful.

Bluetooth Hackers Allegedly Skimmed Millions at Gas Stations

As reported by Wired: Thirteen suspects have been indicted in New York on a gas station skimming scheme that netted them more than $2 million, according to court documents.

The skimming devices, placed on card readers at gas station pumps throughout the southern U.S., recorded credit and debit card data, as well as PINs, which the thieves then used to withdraw more than $2 million from ATMs. They then tried to launder the money through at least 70 different bank accounts, according to the district attorney’s office in New York County.

Some of the skimming devices were placed on pumps at Raceway and Racetrac gas stations throughout Texas, Georgia, and South Carolina. The devices were Bluetooth enabled, so the thieves could simply download the stolen data from the skimming device without having to remove it.

Between March 2012 to March 2013, they used forged cards embossed with the stolen account data to withdraw cash at ATMs in Manhattan, then deposited the money into bank accounts in New York. Co-conspirators in California and Nevada then withdrew the money from ATMs in those states. During that year, the defendants allegedly laundered about $2.1 million.

Garegin Spartalyan, 40, Aram Martirosian, 34, Hayk Dzhandzhapanyan, 40, and Davit Kudugulyan, 42 are the lead defendants in the 426-count indictment charging them with, among other things, money laundering, possession of stolen property, and possession of a forgery device.

The other defendants are each charged with two counts of money laundering.

Living In The Driverless City

As reported by Live Mint:  At the Consumer Electronics Show (CES) in Las Vegas earlier this month, the roulette wheel of innovation landed on something rather old-fashioned and unexpected: the automobile.

In recent decades, cars have been undergoing a gradual transformation from the kinds of mechanical systems Henry Ford might have imagined into computers on wheels. And that transformation is bringing with it a new wave of digital advances—above all, autonomous driving.

The first autonomous (or self-driving) cars date back to the late twentieth century. But recent increases in sophistication and reductions in cost—reflected, for example, in cheap LIDAR systems, which can “see” a street in 3D in a way similar to that of the human eye—are now bringing driverless cars closer to the market.

As we saw last week, several manufacturers are working toward integrating such systems into their fleets, and expect to start selling premium cars with different degrees of autonomy as early as 2016. According to a just-released IHS report, “sometime after 2050” virtually all vehicles on the road might be self-driving.

But what is the drive behind self-driving cars? Are there meaningful benefits beyond the convenience of keeping your hands off the steering wheel and thus being able to read a book, take a nap, or guiltlessly text?

At the CES, journalists were busy snapping pictures of driverless vehicles zooming through the streets of Vegas. But, had they turned their cameras around, they might have captured something far more interesting: the stage upon which the drama of self-driving will take place—the street itself.

Self-driving vehicles promise to have a dramatic impact on urban life, because they will blur the distinction between private and public modes of transportation. “Your” car could give you a lift to work in the morning and then, rather than sitting idle in a parking lot, give a lift to someone else in your family – or, for that matter, to anyone else in your neighbourhood, social-media community, or city.

A recent paper by the Massachusetts Institute of Technology’s SMART Future Mobility team shows that the mobility demand of a city like Singapore—potentially host to the world’s first publicly-accessible feet of self-driving cars—could be met with 30% of its existing vehicles. Furthermore, other researchers in the same group suggest that this number could be cut by another 40% if passengers travelling similar routes at the same time were willing to share a vehicle—an estimate supported by an analysis of New York City Taxis shareability networks. This implies a city in which everyone can travel on demand with just one-fifth of the number of cars in use today.

Such reductions in car numbers would dramatically lower the cost of our mobility infrastructure and the embodied energy associated with building and maintaining it. Fewer cars may also mean shorter travel times, less congestion, and a smaller environmental impact.

The deployment of more intelligent transportation systems promises to deliver similar benefits. Real-time data planning and smart routing are already a reality. Tomorrow’s autonomous vehicles will prompt another wave of innovation, from optimization of road capacity to intersection management. Imagine a world without traffic lights, where vehicular flows “magically” pass through one another and avoid collision.

But, while the world’s mobility challenges will increasingly be met with silicon rather than asphalt, encouraging widespread adoption requires guaranteeing that our streets are as safe—or safer—than they are today. That means that various redundancies must be introduced to ensure that if one component fails, another seamlessly takes over.

Traffic accidents, though rarer, would still be a possibility; in fact, they might be one of the main impediments to implementation of autonomous systems, demanding a restructuring of insurance and liability that could sustain armies of lawyers for years to come.

Finally, there is the fresh issue of digital security. We are all familiar with viruses crashing our computers. But what if the same virus crashes our cars?

All of these issues are urgent, but none of them is insurmountable. They will be resolved in the coming years as autonomy redefines mobility and sparks the next generation of innovations in the field. At that point, the smart money might favor something even more old-fashioned than cars: the city itself.