 From Industrially complex WIRED shills Flying Hummers coming soon for a future of peace In the spring, the futurists at Darpa rethought troop transport. Instead of adding armor or changing the shape to deflect bomb blasts, the agency reasoned, why not let it leap into the sky at the first sign of danger or inconvenience? That’s exactly what Darpa’s “Transformer” project is supposed to be: a mashup of a helicopter, plane and armored truck. And it just came a step closer to reality. AAI Corporation, a Maryland-based aerospace and defense company, won a $3.05 million contract with Darpa to see if it the technology behind the Transformer can, well, get off the ground, Aviation Week reports. Based on so-called “compound helicopter” technology that the company is developing with Carter Aviation Technologies, the gist is that AAI’s design for the Transformer envisions it to carry four soldiers on the road as a car, but the rotor blades on top allow it to take off vertically into the air. The car’s takeoff functions are supposed to be automated, so soldiers or marines don’t have to be aviators to get it off the ground. That’s not all. As Danger Room emerita Sharon Weinberger reported in June, it releases DeLorean-like retractable wings, allowing it to fly faster than a conventional helicopter. “Envision a Humvee-like vehicle with wings that fold out from the side and attach just above the rear door,” AAI Vice President Steven Reid told Weinberger. Elements of three vehicles in one. Read More http://www.wired.com/dangerroom/#ixzz10yeLVw8q  FOR ALL SELF-DRIVING CAR NEWS CLICK HERE Cars as traffic sensors A new algorithm optimizes the dissemination of information about traffic and road conditions through networks of wirelessly connected cars. Data about road and traffic conditions can come from radio stations’ helicopters, the Department of Transportation’s roadside sensors, or even, these days, updates from ordinary people with cell phones. But all of these approaches have limitations: Helicopters are costly to deploy and can observe only so many roads at once, and it could take a while for the effects of congestion to spread far enough that a road sensor will detect them. MIT’s CarTel project is investigating how cars themselves could be used as ubiquitous, highly reliable mobile sensors. At the Association for Computing Machinery’s sixth annual Workshop on Foundations of Mobile Computing on Sept. 16, members of the CarTel team presented a new algorithm that would optimize the dissemination of data through a network of cars with wireless connections. Researchers at Ford are already testing the new algorithm for possible inclusion in future versions of Sync, the in-car communications and entertainment system developed by Ford and Microsoft. For the last four years, CarTel, which is led by computer-science professor Hari Balakrishnan and associate professor Sam Madden, has been collecting data about the driving patterns of Boston-area taxicabs equipped with GPS receivers. On the basis of those data, the CarTel researchers have been developing algorithms for the collection and dissemination of information about the roadways. Once the algorithms have been evaluated and refined, the CarTel researchers plan to test them in an additional, real-world experiment involving networked vehicles. The new algorithm is among those that the group expects to test. Ends at odds Calvin Newport, a postdoc in Balakrishnan’s group, who developed the new algorithm together with Alejandro Cornejo, a grad student in Nancy Lynch’s Theory of Distributed Systems Group, says that previous work on diffusing information through networks of cars tended to assume that, over time, the network would always provide a sequence of connections that could relay data from any one car to any other. The problem is that the CarTel experiment suggests that that isn’t the case. On the other hand, it also demonstrates that two cars that do come within wireless-transmission range of each other will frequently remain near each other for long stretches of time — repeatedly hitting the same lights on the same stretch of road, for instance. A good information-dissemination algorithm should thus ensure that two cars passing each other in opposite directions, with only a fleeting wireless connection, will exchange high-priority data — say, that a tractor trailer has jackknifed across three lanes of traffic on the nearby interstate. On the other hand, it should also ensure that two cars stuck at a light together, with plenty of time on their hands, exchange lower-priority data as well — like the location of a particularly nasty pothole. Newport and Cornejo determined that the best way to meet both requirements was to take advantage of a sequence of numbers known as the binary carry sequence. Technically, each number in the binary carry sequence is the exponent of the highest power of two that will evenly divide the corresponding integer (where “evenly divide” means without a remainder). The integer 1, for instance, can be evenly divided by two to the zero power, or 1, while 2 can be evenly divided by two to the first power, or 2. But three can’t be evenly divided by either 2 or 4, so it, like 1, is divisible only by two to the zero power. The first three digits of the sequence are thus 0, 1, 0. The next nine, as it happens, are 2, 0, 1, 0, 3, 0, 1, 0, 2. READ MORE  Motorists encouraged to spy on rogue drivers (telegraph) Motorists are being encouraged to spy on each other and report incidents of antisocial driving to the police under a new scheme. By Heidi BlakeMotorists are being encouraged to report antisocial driving Photo: PAThousands of drivers have been reported by fellow motorists after being spotted speeding, drink driving or talking on mobile phones. Anyone reported twice in a year could face police action under the scheme, named Operation Crackdown. The culprits could receive a home visit or a warning letter. Sussex Police is trialling the campaign and has already received 20,488 reports from the public. Warning letters have been sent to 2,695, while a further 1,047 have been sanctioned for offences such as having an out-of-date tax disc. The scheme, under which reports are submitted anonymously online, could be rolled out nationally if it is deemed a success. But privacy campaigners have likened it to the tactics of the Stasi in East Germany, which encouraged residents to inform on one another. Dylan Sharpe, of the campaign group Big Brother Watch, warned that Operation Crackdown is "based on unfounded accusations by untrained and possibly prejudiced members of the public". He added: "This scheme is wide open to abuse, ranging from people with minor grudges against neighbours to busybody drivers who think they know what constitutes bad driving." A newsletter promoting the scheme reads: "Are you fed up with anti-social drivers? People who still use their mobile phones while driving, not wearing seat belts or those who insist on getting right up your bumper and are really annoying and dangerous to others." Sussex Police said in a statement: "1,047 drivers have had sanctions imposed on them including 28 for driving while under the influence, 175 vehicles have been seized for being driven without insurance, 376 have been reported to the DVLA for document offences and local councils have seized 64 vehicles for not having current road fund licence". READ MORE  Marines to use autonomous vehicles built by Virginia Tech engineering students using TORC products IMAGE: The Marine Corps Warfighting Laboratory worked closely with Virginia Tech and TORC in the creation of the four Ground Unmanned Support Surrogates (GUSS) that will be used for their ability... Four unmanned autonomous vehicles designed and built by a team of engineering students at Virginia Tech using the TORC Robotic Building Blocks product line, are headed to Hawaii to participate in the 2010 Rim of the Pacific (RIMPAC) war games in July. Fourteen nations, 34 ships, five submarines, more than 100 aircraft, and 20,000 personnel will participate in the biennial RIMPAC exercise June 23 through Aug. 1. The Marine Corps Warfighting Laboratory worked closely with Virginia Tech and TORC in the creation of the four Ground Unmanned Support Surrogates (GUSS) that will be used for their ability to support a platoon of U.S. Marines. The unmanned vehicles can carry up to 1800 pounds and can move at the speed of a troop on foot, or about five miles per hour. The vehicles are designed to re-supply troops, to reduce the actual loads manually carried by Marines, and to provide an immediate means for the evacuation of any casualties in combat. A Marine unit will operate GUSS during the Naval Laboratory's enhanced company operations experimentation that coincides with RIMPAC. Virginia Tech and TORC, a company founded by alumni of the university's robotics program, http://www.torctech.com/ share a very successful track record on their collaborations. Together, they developed autonomous vehicles for the Urban Challenge competition sponsored by the Defense Advanced Research Projects Agency (DARPA) in 2006 and in 2007. "The focus of the collaborations is to leverage the research capabilities of the university with the commercialization capabilities of a small business," said Al Wicks, professor of mechanical engineering (ME) at Virginia Tech and faculty advisor to the team. http://www.me.vt.edu/people/faculty/wicks.html They took home third place honors in 2007 when their vehicle completed DARPA's 60-mile course in less than six hours, with no human intervention allowed past the starting line. The four GUSS vehicles headed to Hawaii are an outgrowth of the technology developed for these DARPA competitions, Wicks said. The sensors have been greatly improved, as well as the perception, planning, and control algorithms to navigate complex environments. The Urban Challenge featured a cooperative environment with well-defined roads for the competition. When the GUSS vehicles are used by the Marine Corps in Hawaii, they will be "off-road and not in a cooperative environment," Wicks said. "This is a big step forward in autonomous vehicles." Michael Fleming, a Virginia Tech ME graduate and the founder and chief executive officer of TORC, explained the team synergism, saying "I believe our team of government, academia, and industry all working together has provided the Marine Corps with a well-balanced solution." As an example, existing algorithms developed by students under previous TORC/Virginia Tech partnerships, were used to create a customized version of the TORC AutonoNav (autonomous navigation system) product to provide the advanced off-road tactical behaviors required to meet the needs of the Marine Corps Warfighting Lab. The rapid development and experimentation on the GUSS project was made possible through the use of TORC's Robotic Building Blocks product line, said David Cutter, marketing manager at TORC. This enabled Virginia Tech engineers to leverage off-the-shelf technologies and focus on system integration challenges. The entire development process was completed in less than a year, with the first prototype delivered for testing in six months. The additional three vehicles were produced in the next five months to be shipped to the RIMPAC exercises. READ MORE  For more automotive news click hereSmart computer learns from video June 23, 2010 The computer recognises the spatial and temporal patterns of sequences of activities in road traffic. Swiss researchers have written a computer programme that is able to analyse temporal and spatial patterns of moving objects, and on top of that is capable of learning. This would be a significant aid in traffic monitoring. The number ten tram crosses the carriageway, makes a sharp bend to the right, and stops in front of the Maschinenlaboratorium building. At the same time, cars roaring down Universitätsstrasse are forced to stop, students scurry across the zebra crossing and the number six tram heading towards the zoo comes around the corner. A typical traffic scene in the city of Zurich. The scene repeats itself in a regular cycle, and spatial and temporal patterns can be recognised. For a person armed with a stopwatch and something to write with, it wouldn’t be too difficult to track and analyse these patterns. For a computer, however, it is a formidable challenge to collect all the data and analyse such a scenario and on top of that to memorise the patterns too. Smart algorithm learns patterns But ETH researchers Daniel Küttel and Michael Breitenstein, together with professors Luc Van Gool and Vittorio Ferrari from the Institute of Image Processing have now developed a computer code - an algorithm - that does precisely this. The software is able to analyse street scenes like this from video images, and map the spatial and temporal patterns that characterise the various road users. The computer can recognise which tram is going past and when, how many cars pass through in its ‘tracks’, and other such details. The computer also registers any deviations from a normal situation. To create this programme, the researchers mounted cameras at a number of junctions around the city of Zurich and recorded hours of video clips. The computer then analysed these video sequences and automatically, i.e. without intervention by the programmers, established rules governing the flow of traffic. The computer had to spend about a day working out the calculations for each hour of video footage that had been recorded. Once the machine had ‘learned’ the standard patterns, however, it was then able to interpret the video recordings in real time. The researchers used comparisons with the tram timetable to ascertain that their programme works extremely accurately. To test the programme’s function, they viewed the timetable on the internet and simultaneously monitored how the computer analysed the traffic scenes. These automatic analyses corresponded exactly with the timetable, to the minute, confirming beyond any doubt that the machine was analysing the video data correctly. Theory was the hard part What sounds simple was actually tough programming work. It took lead author Daniel Küttel, a doctoral candidate of Vittorio Ferrari, nine months to programme the algorithm. “The hardest part of it was processing the theory behind it”, he says. For the researchers, the first priority was to test the concept. Evidently, they did this so well that at the IEEE Conference on Computer Vision and Pattern Recognition held this June in San Francisco, the IEEE professional organisation included this work in the top five per cent of 1,200 contributions submitted. READ MORE  FROM NEWS. AU: IBM applies for right to stop your car at intersections, week after researchers hack car THE days of running red lights may be numbered. Which is obviously a good thing in the obvious sense that things could get a little safer, but the method by which it may come about is causing some concern. If you're at all in fear of a Skynet future controlled entirely by computers such as that featured in the Terminator franchise, look away now. IBM has applied for a patent that covers "a method for managing engines in response to a traffic signal". If you're crawling through traffic in 2025 and approach a traffic light, IBM hopes it will be able to take control of your car. And according to the patent, you won't be able to go again until it lets you. Which sounds relatively harmless - it's something we do, or should do, anyway - but the issue of computer-controlled cars had some uneasy light thrown on it last week, when researchers at the universities of Washington and California enjoyed themselves at the expense of current models. READ MORE ON THIS STORY Also check out the UK's SARTE initiative involving the same concept |    See all tech news here
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