Price: Not for sale
Power plant: Hydrogen fuel cell
Overall fuel economy: N/A
For years, automakers have rhapsodized about how our cars would become mobile offices and living spaces. And then they botched even the simple stuff, like letting you dial up the Backstreet Boys from your iPod on the car’s sound system. But fear not. The Mercedes-Benz F 015 will be the rolling roost of your dreams. You’ll just have to wait at least until 2030, when Mercedes thinks this kind of hydrogen-powered, fully autonomous vehicle will become viable.
Bigger than an S-Class, the Benz concept looks like a Clockwork Orange hipster lounge, with its walnut-veneered floor and wall-wrapping touch and gesture displays. Mercedes sees it as a retreat that will maximize privacy or productivity in the hectic urban zones of the future. The mod theme continues with two front white-leather-clad lounge seats that swivel rearward (after all, the “driver” usually won’t need to attend to the road). The steering wheel telescopes into the dash during autonomous mode. And any passenger can take charge of vehicle functions, such as speed and which of the 360-degree views to project inside the car.
The concept car makes clever use of optical technologies to communicate with cars and pedestrians. A forward-looking laser, for example, can beam messages onto the pavement, including a whimsical image of a zebra crossing or the words “please go ahead.” You get into the car using a smartphone app, which opens enormous clamshell-style portals for easy access to the lounge space inside. A hydrogen-fueled F (for “fuel”) cell plug-in hybrid drive system could deliver a 1,100-kilometer driving range, Mercedes says, including 200 km on battery power.
If you care to trust Mercedes’s crystal ball, by 2030 hydrogen cars will be a common sight. Unless gasoline still costs $2 a gallon. Or if Telsa has its way.
310
Saturday, April 9, 2016
Friday, April 8, 2016
IBM's Rodent Brain Chip
IBM has created neuromorphic chips. In August of last year, the project leader Dharmendra Modha and his cognitive computing team shared their unusual creations with the outside world, running a three-week “boot camp” for academics and government researchers at an IBM R&D lab on the far side of Silicon Valley. At a conference last year, this eclectic group of computer scientists explored the particulars of IBM’s architecture and have begun building software for the chip dubbed TrueNorth. In an interview with WIRED last year, Modha showed them one of the neuromorphic chips. "About the size of a bathroom medicine cabinet, it rests on a table against the wall, and thanks to the translucent plastic on the outside, I can see the computer chips and the circuit boards and the multi-colored lights on the inside. It looks like a prop from a ’70s sci-fi movie, but Modha describes it differently." "You’re looking at a small rodent," he said. He means the brain of a small rodent—or, at least, the digital equivalent. The chips on the inside are designed to behave like neurons—the basic building blocks of biological brains. Modha says the system in front of us spans 48 million of these artificial nerve cells, roughly the number of neurons packed into the head of a rodent.
Some researchers who got their hands on the chip at an engineering workshop in Colorado the previous month have already fashioned software that can identify images, recognize spoken words, and understand natural language. Basically, they’re using the chip to run “deep learning” algorithms, the same algorithms that drive the internet’s latest AI services, including the face recognition on Facebook and the instant language translation on Microsoft’s Skype. But the promise is that IBM’s chip can run these algorithms in smaller spaces with considerably less electrical power, letting us shoehorn more AI onto phones and other tiny devices, including hearing aids and, well, wristwatches.
“What does a neuro-synaptic architecture give us? It lets us do things like image classification at a very, very low power consumption,” says Brian Van Essen, a computer scientist at the Lawrence Livermore National Laboratory who’s exploring how deep learning could be applied to national security. “It lets us tackle new problems in new environments.”
The TrueNorth is part of a widespread movement to refine the hardware that drives deep learning and other AI services. Companies like Google and Facebook and Microsoft are now running their algorithms on machines backed with GPUs (chips originally built to render computer graphics), and they’re moving towards FPGAs (chips you can program for particular tasks). For Peter Diehl, a PhD student in the cortical computation group at ETH Zurich and University Zurich, TrueNorth outperforms GPUs and FPGAs in certain situations because it consumes so little power.
The main difference, says Jason Mars, a professor of a computer science at the University of Michigan, is that the TrueNorth dovetails so well with deep-learning algorithms. These algorithms mimic neural networks in much the same way IBM’s chips do, recreating the neurons and synapses in the brain. One maps well onto the other. “The chip gives you a highly efficient way of executing neural networks,” says Mars, who declined an invitation to this month’s boot camp but has closely followed the progress of the chip.
That said, the TrueNorth suits only part of the deep learning process—at least as the chip exists today—and some question how big an impact it will have. Though IBM is now sharing the chips with outside researchers, it’s years away from the market. For Modha, however, this is as it should be. As he puts it: “We’re trying to lay the foundation for significant change.”
615
Some researchers who got their hands on the chip at an engineering workshop in Colorado the previous month have already fashioned software that can identify images, recognize spoken words, and understand natural language. Basically, they’re using the chip to run “deep learning” algorithms, the same algorithms that drive the internet’s latest AI services, including the face recognition on Facebook and the instant language translation on Microsoft’s Skype. But the promise is that IBM’s chip can run these algorithms in smaller spaces with considerably less electrical power, letting us shoehorn more AI onto phones and other tiny devices, including hearing aids and, well, wristwatches.
“What does a neuro-synaptic architecture give us? It lets us do things like image classification at a very, very low power consumption,” says Brian Van Essen, a computer scientist at the Lawrence Livermore National Laboratory who’s exploring how deep learning could be applied to national security. “It lets us tackle new problems in new environments.”
The TrueNorth is part of a widespread movement to refine the hardware that drives deep learning and other AI services. Companies like Google and Facebook and Microsoft are now running their algorithms on machines backed with GPUs (chips originally built to render computer graphics), and they’re moving towards FPGAs (chips you can program for particular tasks). For Peter Diehl, a PhD student in the cortical computation group at ETH Zurich and University Zurich, TrueNorth outperforms GPUs and FPGAs in certain situations because it consumes so little power.
The main difference, says Jason Mars, a professor of a computer science at the University of Michigan, is that the TrueNorth dovetails so well with deep-learning algorithms. These algorithms mimic neural networks in much the same way IBM’s chips do, recreating the neurons and synapses in the brain. One maps well onto the other. “The chip gives you a highly efficient way of executing neural networks,” says Mars, who declined an invitation to this month’s boot camp but has closely followed the progress of the chip.
That said, the TrueNorth suits only part of the deep learning process—at least as the chip exists today—and some question how big an impact it will have. Though IBM is now sharing the chips with outside researchers, it’s years away from the market. For Modha, however, this is as it should be. As he puts it: “We’re trying to lay the foundation for significant change.”
615
Sunday, April 3, 2016
2016's Top Tech Cars: Ford GT
Price: US $400,000
Power plant: 3.5-L V-6 with dual turbochargers; 485 kW (650 hp)
Overall fuel economy: N/A
Nearly a half century ago, the Ford GT40 went to the 24 Hours of LeMans and crushed mighty Ferrari, sparking an enduring legend. Now Ford looks for a LeMans déjà vu this summer with a reborn racing GT, followed by 250 annual copies of a roughly US $400,000 scissor-doored wonder car for the street.
The GT eschews a V-8 for a downsized twin-turbo V-6 based on its Daytona-winning LMP2 race engine. Ford is promising the best power-to-weight ratio of any production car in the world, with a hand-laid carbon-fiber tub and body for this mid-engine monster.
An active suspension lets the Ford hunker down at triple-digit speeds to reduce drag, while an active air brake at the rear rises and angles as needed to boost aero downforce or slow the car into corners. The gorgeous fuselage is billionaire bait, but the bravura style is wedded to pure function. A curved pair of flying buttresses perform dual tricks: The winged roof channels direct air to the rear spoiler, and their hollow sections contain piping for the turbo intercoolers: Engine intake air is hoovered from beneath the car, compressed into the turbos, then snaked through the winglets and down again to hyperventilate the V-6. Heated air from the intercoolers flows rearward and exits through tubes in the center of the rear taillights.
It’s all executed so beautifully that we were pleased to gawp at the thing at the recent Detroit Auto Show. But I’ll be happier when Ford finally lets us drive it.
270
Power plant: 3.5-L V-6 with dual turbochargers; 485 kW (650 hp)
Overall fuel economy: N/A
Nearly a half century ago, the Ford GT40 went to the 24 Hours of LeMans and crushed mighty Ferrari, sparking an enduring legend. Now Ford looks for a LeMans déjà vu this summer with a reborn racing GT, followed by 250 annual copies of a roughly US $400,000 scissor-doored wonder car for the street.
The GT eschews a V-8 for a downsized twin-turbo V-6 based on its Daytona-winning LMP2 race engine. Ford is promising the best power-to-weight ratio of any production car in the world, with a hand-laid carbon-fiber tub and body for this mid-engine monster.
An active suspension lets the Ford hunker down at triple-digit speeds to reduce drag, while an active air brake at the rear rises and angles as needed to boost aero downforce or slow the car into corners. The gorgeous fuselage is billionaire bait, but the bravura style is wedded to pure function. A curved pair of flying buttresses perform dual tricks: The winged roof channels direct air to the rear spoiler, and their hollow sections contain piping for the turbo intercoolers: Engine intake air is hoovered from beneath the car, compressed into the turbos, then snaked through the winglets and down again to hyperventilate the V-6. Heated air from the intercoolers flows rearward and exits through tubes in the center of the rear taillights.
It’s all executed so beautifully that we were pleased to gawp at the thing at the recent Detroit Auto Show. But I’ll be happier when Ford finally lets us drive it.
270
Saturday, April 2, 2016
2016's Top Tech Cars: Audi Autonomous RS7
Price: US $136,650
Power train: 96-kW (129-hp) AC electric motor with 1.5-L 170-kW (228-hp) three-cylinder gasoline engine
Overall fuel economy: 8.4 L/100 km (28 mpg) on gasoline; electric equivalent of 3.1 L/100 km (76 mpge)
Here in Spain, the man-vs.-machine competition will be at higher speed and for higher stakes. I’m about to take on Robby, the autonomous RS7 sport sedan that’s designed to rock a racetrack at speeds that would blister Google’s cartoonish bubble car. If a human driver can’t keep up, it occurs to me, then our obsolescence draws that much closer. It’s only a matter of time before governments and automakers pry the ignition keys out of our fallible, accident-prone hands for good.
Robby is looking cool and confident in the pits at Parcmotor Castelloli, near Barcelona. And for good reason: The Audi weighs 400 kilograms (882 pounds) less than Bobby, the RS7 that holds the world speed record for autonomous cars, at 240 kilometers per hour (149 miles per hour).
I take to calling the newer car Robby the Robot, after the glass-skulled automaton from the 1956 sci-fi movie Forbidden Planet. Miklos Kiss, Audi’s head of predevelopment for driver assistance systems, introduces us to his two autonomous brainchildren. Popping Bobby’s hatch, we find it full to bursting with computer gear. Robby’s, in contrast, has plenty of room left over for luggage. There’s a single MicroAutoBox brain and power supply and two other small computers.
Incredibly, Audi’s latest differential GPS unit can fix Robby’s position to within 2 centimeters, vastly better than today’s GPS standard of roughly 1.5 meters. This price-no-object system also uses redundant cameras to triangulate and thus to confirm the car’s location. Keeping a fully autonomous vehicle safely within lanes will require zeroing in to 50 cm (around 20 inches).
Controllers adjust the engine, electric steering, transmission, and brakes, with redundant fail-safes: There’s a spare power supply and brake controllers if the first ones conk out. A 4.0-liter biturbo V-8 produces a villainous exhaust note that echoes off the dusty canyon walls.
I slide into Robby’s shotgun seat with some trepidation, thinking that the driver’s side will remain spookily unoccupied. But surprise! There’s an Audi engineer in the seat, along for the passive ride but holding a plunger connected to a cord. If something goes wrong and he lets go of the plunger’s button, the Audi will slow and halt on the track. Theoretically.
The checkered flag waves. The RS7 launches itself down the front straight and charges into the first corner, the steering wheel twirling, the ghosts fully in charge of this machine. The brake and throttle pedals aren’t moving at all because the computer commands are bypassing the old analog connections.
Before I arrived, Audi engineers had manually driven Robby around this Spanish track to measure its barriers and “geo-fence” a safe zone beyond which Robby will not go. Like a real-life slot car, the Audi locks onto its programmed track line, its path varying by only a few centimeters. Yet the RS7 also reacts in real time to conditions such as a slippery track or wearing tires, dialing back power or correcting the steering if it begins to slide off its satellite-guided path.
/image/MjczMTc2NA
Photo: Audi
Brain in a Boot: The Audi RS7’s computing hardware fills the trunk space.
Robby turns out to be a smoothy, a race-instructor type who puts up great, flowing lap times yet keeps the car utterly balanced and composed. Its dead-consistent laps vary by less than 0.3 second, including a best this day of 2:09.2.
Now, it’s my turn, and I jump aboard a production Audi RS7. Suddenly, I’m John Connor taking on Skynet, a human fighting for his increasingly pitiful and superfluous species. After one reconnaissance lap on this unfamiliar track, I turn the Audi loose. Please, Lord, don’t let me lose to a stinking machine.
I drive back into the pits and dash over to the timer. A few Audi engineers applaud, a bit grudgingly, I’m thinking. But my lap of 2:05.4 is nearly 4 seconds quicker than Robby’s. Even after hundreds of laps in recent weeks, Robby’s best is still 2 seconds behind my first trip around Castelloli. Take that, you remorseless Terminator, German accent and all.
But as my adrenaline subsides, I’m forced to concede that adrenaline is among my biological advantages. Robby may know speed, but the words “race” or “win” simply aren’t part of his vocabulary. Yet.
Where I punished the tires and pushed the limits, Robby stayed emotionless, programmed to run safe, endlessly repeatable laps. The last thing Audi needs is for a self-driving car to disintegrate against a wall or injure its passengers, setting back autonomous driving by a few years. A few tweaks of the algorithms, a few more generations, and Robby’s offspring will be chip-enabled Michael Schumachers. (Please, Audi, name your next car Ricky Bobby.) They’ll scan and pick out us humans up ahead and make us eat their digital dust, if they choose. Or they’ll chauffeur our miserable hides straight to the police if we act up, as did Tom Cruise’s 2054 Lexus in Minority Report. What’s to stop them?
Yes, the rise of the machines seems inevitable. But as my race with Robby showed, some humans will still put up a fight before going to the scrap heap.
929
Friday, April 1, 2016
The Tesla Model 3
The $35,000 Tesla Model 3 is finally here. It is sleek, quick as hell, and meant for the masses. And it is the most important car the company will ever build.
The Model 3 is the car Tesla Motors has promised since the company’s founding, the car that CEO Elon Musk is convinced will push EVs into the mainstream and the technology to an inflection point. Not to put too fine a point on it, it is the car Musk believes will change the world.
“It’s very important to accelerate the transition to sustainable transport,” Musk said on stage. “This is really important for the future of the world.”
In person and on paper, the Model 3 is a stunner. It’s a handsome sedan, with four doors and five seats, and all the comfort and practicality you’d expect of an upscale mid-size sedan. The battery is good for a 0 to 60 mph time under six seconds, a range of 215 miles. It’s packed with tech, stylish, and a bargain if Tesla can deliver it at the $27,500 base price Musk promises you’ll pay after the federal tax credit.The specs and price are key, because so far Tesla Motors has aimed squarely at the affluent. The company’s first three models—the innovative Roadster sports car, exquisite Model S sedan, and tech-slinging Model X SUV—made electric cars fun, cool, and compelling. The Model 3 is meant to do something greater: sell the masses on electric propulsion.
Tesla is hardly alone in hoping to do this and, frankly, got beaten in the race to build a $30K EV wth a triple-digit range by General Motors. In January, the Detroit stalwart introduced the 2017 Chevrolet Bolt, a battery electric hatchback with a range of 200 miles and a price of 30 grand after the $7,500 federal tax credit.
Still, Musk isn’t the slightest bit worried and, to be fair, has little reason to be. The Bolt is lovely, but Tesla has a proven ability to get people excited, and there’s no denying the company has a cachet many automakers do not. You don’t often see people lining up outside dealerships simply to place a $1,000 deposit on a car they haven’t even seen—something that happened at many Tesla stores this week. By the time Musk pulled the sheet off the color Model 3 at the sprawling Space X campus here in Hawthorne, California, 115,000 customers had put their money down.
“They’ll absolutely have a wow factor, because it’s Tesla,” says Gary Silberg, an automotive analyst with KPMG. “They’ll know how to market it, and from that perspective, there’s no doubt in my mind it’s gonna be a big success.”
Tesla doesn’t have to worry about creating a market for the 3. Nor does it have to worry about actually building it. No, the upstart automaker has to do something much harder.
If the company is to truly influence, let alone change, how humanity moves around, it must become more than a niche automaker building luxury vehicles and playing gadfly to the big players. That means producing vehicles on a massive scale and generating sustainable profits. To do that, Tesla must think and act a lot more like the very automakers Musk is so quick to ridicule as out-dated and old-fashioned.
It’s time for Tesla to grow up.
558
The Model 3 is the car Tesla Motors has promised since the company’s founding, the car that CEO Elon Musk is convinced will push EVs into the mainstream and the technology to an inflection point. Not to put too fine a point on it, it is the car Musk believes will change the world.
“It’s very important to accelerate the transition to sustainable transport,” Musk said on stage. “This is really important for the future of the world.”
In person and on paper, the Model 3 is a stunner. It’s a handsome sedan, with four doors and five seats, and all the comfort and practicality you’d expect of an upscale mid-size sedan. The battery is good for a 0 to 60 mph time under six seconds, a range of 215 miles. It’s packed with tech, stylish, and a bargain if Tesla can deliver it at the $27,500 base price Musk promises you’ll pay after the federal tax credit.The specs and price are key, because so far Tesla Motors has aimed squarely at the affluent. The company’s first three models—the innovative Roadster sports car, exquisite Model S sedan, and tech-slinging Model X SUV—made electric cars fun, cool, and compelling. The Model 3 is meant to do something greater: sell the masses on electric propulsion.
Tesla is hardly alone in hoping to do this and, frankly, got beaten in the race to build a $30K EV wth a triple-digit range by General Motors. In January, the Detroit stalwart introduced the 2017 Chevrolet Bolt, a battery electric hatchback with a range of 200 miles and a price of 30 grand after the $7,500 federal tax credit.
Still, Musk isn’t the slightest bit worried and, to be fair, has little reason to be. The Bolt is lovely, but Tesla has a proven ability to get people excited, and there’s no denying the company has a cachet many automakers do not. You don’t often see people lining up outside dealerships simply to place a $1,000 deposit on a car they haven’t even seen—something that happened at many Tesla stores this week. By the time Musk pulled the sheet off the color Model 3 at the sprawling Space X campus here in Hawthorne, California, 115,000 customers had put their money down.
“They’ll absolutely have a wow factor, because it’s Tesla,” says Gary Silberg, an automotive analyst with KPMG. “They’ll know how to market it, and from that perspective, there’s no doubt in my mind it’s gonna be a big success.”
Tesla doesn’t have to worry about creating a market for the 3. Nor does it have to worry about actually building it. No, the upstart automaker has to do something much harder.
If the company is to truly influence, let alone change, how humanity moves around, it must become more than a niche automaker building luxury vehicles and playing gadfly to the big players. That means producing vehicles on a massive scale and generating sustainable profits. To do that, Tesla must think and act a lot more like the very automakers Musk is so quick to ridicule as out-dated and old-fashioned.
It’s time for Tesla to grow up.
558
Saturday, March 26, 2016
The super-efficient autonomous intersection
As you know if you've driven anywhere ever, traffic lights are part of a vast conspiracy designed to make it as difficult and time consuming as possible for you to get where you want to go. Lights change from green to red because someone might be coming from another direction, which isn't a very efficient way to run things, since you spend so much of your travel time either slowing down, speeding up, or stopped uselessly.
The only reason that we have to suffer through red lights is that humans in general aren't aware enough, quick enough, or kind enough to safely and reliably take turns through intersections at speed. Autonomous cars, which are far better at both driving and cooperating, don't need these restrictions. So, with a little bit of communication and coordination, they'll be able to blast through even the most complex intersections while barely slowing down.
These autonomous intersections are "slot-based," which means that they operate similarly to the way that air traffic control systems at airports coordinate landing aircraft. Air traffic controllers communicate with all incoming aircraft, and assign each one of them a specific place in the landing pattern. The individual aircraft speed up or slow down on their approach to the pattern, such that they enter it at the right time, in the right slot, and the overall pattern flows steadily. This is important, since fixed-wing aircraft tend to have trouble coming to a stop before landing.
The reason that we can't implement this system in cars is twofold: we don't have a centralized intersection control system to coordinate between vehicles, and vehicles (driven by humans) don't communicate their intentions in a reliable manner. But with autonomous cars, we could make this happen for real, and if we do, the advantages would be significant. Using a centralized intersection management and vehicle communication system, slot-based intersections like the one in the video above could significantly boost intersection efficiency. We've known this anecdotally for a while, but a new paper from researchers at MIT gives our hunches about the increase in efficiency empirical heft. The MIT team also suggests ways in which traffic flow through intersections like these could be optimized.
Rather than designing traffic management systems so that they prioritize vehicle arrival times on a first come, first served basis, the researchers suggest sending vehicles through in batches—especially as traffic gets heavier. This would involve a slight delay for individual vehicles (since they may have to coordinate with other vehicles to form a batch), but it's more efficient overall, since batches of cars can trade intersection time better than single vehicles can. The video above shows the batch method, while the video below (from 2012 research at UT Austin) shows a highly complex intersection with coordination of single cars rather than batches.
Simulations suggest that a slot-based system sending through groups of cars could double the capacity of an intersection, while also significantly reducing wait times. In the simplest case (an intersection of two single-lane roads), cars arriving at a rate of one every 3 seconds would experience an average delay of about 5 seconds if they had to wait for a traffic light to turn green. An autonomously controlled intersection would drop that wait time to less than a second. Not bad. But the control system really starts to show its worth as traffic increases. If a car arrives every 2.5 seconds, the average car will be delayed about 10 seconds by a traffic light, whereas the slot-based intersection would hold it up for a second and a half. And as the arriving cars start to overload the capacity of our little intersection at 1 car every 2 seconds, you'd be stuck there for 99 seconds (!) if there's a light, but delayed only 2.5 seconds under autonomous slot-based control.
We should point out that this only works if all of the cars traveling through the intersection are autonomous. One human trying to get through this delicately choreographed scrum would probably cause an enormous number of accidents due to both lack of coordination and unpredictability. For this reason, it seems likely that traffic lights aren't going to disappear until humans finally give up driving. An interim step, though, might be traffic lights that stay green (in all directions) as long as only autonomous cars are passing through them, reverting to a traditional (frustrating and inefficient, that is) state of operation when a human approaches.
The researchers point out that the advantages of slot-based control go beyond just saving time: they reduce fuel consumption, and along with it, the amount of carbon that would otherwise get pumped into the atmosphere by legions of cars idling at traffic lights.
It will take a lot of work to implement something like this. And because it's heavily dependent on having autonomous cars replace today’s human-controlled vehicles, let's hurry up and let the robots take over so that we can all benefit.
882
The only reason that we have to suffer through red lights is that humans in general aren't aware enough, quick enough, or kind enough to safely and reliably take turns through intersections at speed. Autonomous cars, which are far better at both driving and cooperating, don't need these restrictions. So, with a little bit of communication and coordination, they'll be able to blast through even the most complex intersections while barely slowing down.
These autonomous intersections are "slot-based," which means that they operate similarly to the way that air traffic control systems at airports coordinate landing aircraft. Air traffic controllers communicate with all incoming aircraft, and assign each one of them a specific place in the landing pattern. The individual aircraft speed up or slow down on their approach to the pattern, such that they enter it at the right time, in the right slot, and the overall pattern flows steadily. This is important, since fixed-wing aircraft tend to have trouble coming to a stop before landing.
The reason that we can't implement this system in cars is twofold: we don't have a centralized intersection control system to coordinate between vehicles, and vehicles (driven by humans) don't communicate their intentions in a reliable manner. But with autonomous cars, we could make this happen for real, and if we do, the advantages would be significant. Using a centralized intersection management and vehicle communication system, slot-based intersections like the one in the video above could significantly boost intersection efficiency. We've known this anecdotally for a while, but a new paper from researchers at MIT gives our hunches about the increase in efficiency empirical heft. The MIT team also suggests ways in which traffic flow through intersections like these could be optimized.
Rather than designing traffic management systems so that they prioritize vehicle arrival times on a first come, first served basis, the researchers suggest sending vehicles through in batches—especially as traffic gets heavier. This would involve a slight delay for individual vehicles (since they may have to coordinate with other vehicles to form a batch), but it's more efficient overall, since batches of cars can trade intersection time better than single vehicles can. The video above shows the batch method, while the video below (from 2012 research at UT Austin) shows a highly complex intersection with coordination of single cars rather than batches.
Simulations suggest that a slot-based system sending through groups of cars could double the capacity of an intersection, while also significantly reducing wait times. In the simplest case (an intersection of two single-lane roads), cars arriving at a rate of one every 3 seconds would experience an average delay of about 5 seconds if they had to wait for a traffic light to turn green. An autonomously controlled intersection would drop that wait time to less than a second. Not bad. But the control system really starts to show its worth as traffic increases. If a car arrives every 2.5 seconds, the average car will be delayed about 10 seconds by a traffic light, whereas the slot-based intersection would hold it up for a second and a half. And as the arriving cars start to overload the capacity of our little intersection at 1 car every 2 seconds, you'd be stuck there for 99 seconds (!) if there's a light, but delayed only 2.5 seconds under autonomous slot-based control.
We should point out that this only works if all of the cars traveling through the intersection are autonomous. One human trying to get through this delicately choreographed scrum would probably cause an enormous number of accidents due to both lack of coordination and unpredictability. For this reason, it seems likely that traffic lights aren't going to disappear until humans finally give up driving. An interim step, though, might be traffic lights that stay green (in all directions) as long as only autonomous cars are passing through them, reverting to a traditional (frustrating and inefficient, that is) state of operation when a human approaches.
The researchers point out that the advantages of slot-based control go beyond just saving time: they reduce fuel consumption, and along with it, the amount of carbon that would otherwise get pumped into the atmosphere by legions of cars idling at traffic lights.
It will take a lot of work to implement something like this. And because it's heavily dependent on having autonomous cars replace today’s human-controlled vehicles, let's hurry up and let the robots take over so that we can all benefit.
882
Friday, March 25, 2016
Nanocones and what they mean for solar power
Researchers at the Royal Melbourne Institute of Technology (RMIT University) in Australia have created an entirely new nanostructure they have dubbed a “nanocone”. It combines the upside-down physics of topological insulators with the easier-to-explain process of plasmonics. The result is a nanomaterial that can be used with silicon-based photovoltaics to increase their light absorption properties.
Topological insulators have the peculiar property of behaving as insulators on the inside but conductors on the outside and plasmonics exploits the oscillations in the density of electrons that are generated when photons hit a metal surface. What the RMIT researchers have done by bringing these worlds together is create a plasmonic nanostructure that has a core-shell structure that lends itself to being topological insulator.
“This is the first time that a nanocone with intrinsically core-shell structure has been fabricated,” said Min Gu, the RMIT professor who led the research, in an e-mail interview with IEEE Spectrum. “The nanocone has a topologically protected metallic shell and a dielectric (insulating) core. They do not need a particular fabrication method and the unique nanostructure has the intrinsic properties of topological insulators.”
The topological insulator nanocone arrays could enhance the light absorption of solar cells by focusing incident sunlight into the silicon, according to Gu.
In research described in the journal Science Advances, this enhanced light absorption is achieved by the insulating core of the cone providing an ultrahigh refractive index in the near-infrared frequency range. Meanwhile, the metallic shell provides a strong plasmonic response and strong backward light scattering in the visible frequency range.
The researchers predict that then when a nanocone array is integrated into a silicon thin-film solar cell, it can help enhance light absorption for the cell up to 15 percent in the ultraviolet and visible ranges.
“With the enhanced light absorption, both the short circuit current and photoelectric conversion efficiency could be enhanced,” said Gu.
In future research, Gu and his colleagues plan to investigate plasmonics in other types of topological insulator nanostructures, such as nano-spheres and nano-cylinders and try to achieve plasmonic nanostructures that respond to a broad spectrum of light: from ultraviolet down to THz in all in a single core-shell nanostructure. He added: “In particular, we want to apply these nanostructures into ultra-thin PV devices.”
379
Topological insulators have the peculiar property of behaving as insulators on the inside but conductors on the outside and plasmonics exploits the oscillations in the density of electrons that are generated when photons hit a metal surface. What the RMIT researchers have done by bringing these worlds together is create a plasmonic nanostructure that has a core-shell structure that lends itself to being topological insulator.
“This is the first time that a nanocone with intrinsically core-shell structure has been fabricated,” said Min Gu, the RMIT professor who led the research, in an e-mail interview with IEEE Spectrum. “The nanocone has a topologically protected metallic shell and a dielectric (insulating) core. They do not need a particular fabrication method and the unique nanostructure has the intrinsic properties of topological insulators.”
The topological insulator nanocone arrays could enhance the light absorption of solar cells by focusing incident sunlight into the silicon, according to Gu.
In research described in the journal Science Advances, this enhanced light absorption is achieved by the insulating core of the cone providing an ultrahigh refractive index in the near-infrared frequency range. Meanwhile, the metallic shell provides a strong plasmonic response and strong backward light scattering in the visible frequency range.
The researchers predict that then when a nanocone array is integrated into a silicon thin-film solar cell, it can help enhance light absorption for the cell up to 15 percent in the ultraviolet and visible ranges.
“With the enhanced light absorption, both the short circuit current and photoelectric conversion efficiency could be enhanced,” said Gu.
In future research, Gu and his colleagues plan to investigate plasmonics in other types of topological insulator nanostructures, such as nano-spheres and nano-cylinders and try to achieve plasmonic nanostructures that respond to a broad spectrum of light: from ultraviolet down to THz in all in a single core-shell nanostructure. He added: “In particular, we want to apply these nanostructures into ultra-thin PV devices.”
379
Subscribe to:
Posts (Atom)