road


Life in the Fast Lane

AI Magazine

Giving robots the ability to operate in the real world has been, and continues to be, one of the most difficult tasks in AI research. Since 1987, researchers at Carnegie Mellon University have been investigating one such task. Their research has been focused on using adaptive, vision-based systems to increase the driving performance of the Navlab line of on-road mobile robots. This research has led to the development of a neural network system that can learn to drive on many road types simply by watching a human teacher. This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.


Three Anecdotes from the DARPA Autonomous Land Vehicle Project

AI Magazine

This was a large applied research effort that presented many opportunities for unusual experiences. In one such experience, I was called in, at the last minute, to help improve our ALV proposal. The proposal was a 300-page document that segued smoothly from problem description to corporate capabilities and managerial plan, omitting any mention of technical approach. This taught me a rule of thumb I have seen validated many times: the larger the project (in dollars and scope), the poorer the technical proposal. In a second experience, I was demonstrating a dynamic programming algorithm at a quarterly review.


Autonomous Driving in Traffic: Boss and the Urban Challenge

AI Magazine

In this article we introduce Boss, the autonomous vehicle that won the challenge. Boss is a complex artificially intelligent software system embodied in a 2007 Chevy Tahoe. To navigate safely, the vehicle builds a model of the world around it in real time. This model is used to generate safe routes and motion plans both on roads and in unstructured zones. An essential part of Boss's success stems from its ability to safely handle both abnormal situations and system glitches.


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AI Magazine

Knowledge-Based Backtracking," is a principal researcher for the Advanced Technology Center, Boeing Computer Services, P.O. Preston A. Cox, coauthor of "Real-Time Knowledge-Based Systems," is a scientific programmer specialist for Lockheed's Space System Division in Sunnyvale, California. Rebecca Gomez is a graduate research assistant at the Computing Research Laboratory, New Mexico State University, Las Cruces, New Mexi-0.88003. Kentieth M. Kahn, author of the workshop report "Concurrent Logic Programming, Metaprogramming, and Open Systems, " is a member of the research staff at Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, California 94304. His interests include computer language design, distributed computing, logic programming, parallel computations, and object-oriented programming.


AI in 2018: Experts predict what happens next

#artificialintelligence

Perhaps no other technology was as disruptive as artificial intelligence in 2017. 'Machine learning,' 'neural network,' and'data bias' became commonplace terms in the headlines of mainstream media outlets, signifying the machines had arrived. And with them comes an uncertain future. We reached out to several experts to tell us what to expect from AI in 2018. These educational robots make a lot of sense from a business perspective-- there's a big market for STEM education toys and they're more affordable to produce as they don't require massive CPU, lots of sensors or advanced AI.


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#artificialintelligence

Perhaps no other technology was as disruptive as artificial intelligence in 2017. 'Machine learning,' 'neural network,' and'data bias' became commonplace terms in the headlines of mainstream media outlets, signifying the machines had arrived. And with them comes an uncertain future. We reached out to several experts to tell us what to expect from AI in 2018. These educational robots make a lot of sense from a business perspective-- there's a big market for STEM education toys and they're more affordable to produce as they don't require massive CPU, lots of sensors or advanced AI.


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#artificialintelligence

The media loves to frame matters of technology in quite dystopian terms, so it's perhaps not surprising that coverage of autonomous vehicles has tended to focus on both the safety implications of the technology and the number of driving jobs that might be lost. It's a narrative that seems to demand perfection from the technology before it can be rolled out en-masse on public roads. Such thinking has distinct dangers of its own, with a recent from the RAND Corporation highlighting how delaying the launch of autonomous technology until perfection is achieved will cost many thousands of lives per year. The report argues that even a 10% improvement on human drivers could save thousands of deaths on our roads, thus rendering it morally questionable whether it's wise to demand technology that is 90% and above better than human drivers. The figures were arrived at by examining hundreds of possible futures and the changing safety requirements for autonomous vehicle introduction, with estimated road fatalities extrapolated for each potential scenario.


Hackers are the real obstacle for self-driving vehicles

#artificialintelligence

Before autonomous trucks and taxis hit the road, manufacturers will need to solve problems far more complex than collision avoidance and navigation (see "10 Breakthrough Technologies 2017: Self-Driving Trucks"). These vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning (see "AI Fight Club Could Help Save Us from a Future of Super-Smart Cyberattacks"). As consensus grows that autonomous vehicles are just a few years away from being deployed in cities as robotic taxis, and on highways to ease the mind-numbing boredom of long-haul trucking, this risk of attack has been largely missing from the breathless coverage. It reminds me of numerous articles promoting e-mail in the early 1990s, before the newfound world of electronic communications was awash in unwanted spam. Back then, the promise of machine learning was seen as a solution to the world's spam problems.


AI shouldn't believe everything it hears

#artificialintelligence

Artificial intelligence can accurately identify objects in an image or recognize words uttered by a human, but its algorithms don't work the same way as the human brain--and that means that they can be spoofed in ways that humans can't. New Scientist reports that researchers from Bar-Ilan University in Israel and Facebook's AI team have shown that it's possible to subtly tweak audio clips so that a human understands them as normal but a voice-recognition AI hears something totally different. The approach works by adding a quiet layer of noise to a sound clip that contains distinctive patterns a neural network will associate with other words. The team applied its new algorithm, called Houdini, to a series of sound clips, which it then ran through Google Voice to have them transcribed. Her bearing was graceful and animated she led her son by the hand and before her walked two maids with wax lights and silver candlesticks.


uber-buys-24000-volvos-trolley-problems-get-scarier-and-liability

Robohub

Uber and Volvo announced an agreement where Uber will buy, in time, up to 24,000 specially built Volvo XC90s which will run Uber's self-driving software and, presumably, offer rides to Uber customers. While the rides are some time away, people have made note of this for several reasons. I'm not clear who originally said it -- I first heard it from Marc Andreesen -- but "the truest form of a partnership is called a purchase order." In spite of the scores of announced partnerships and joint ventures announced to get PR in the robocar space, this is a big deal, but it's a sign of the sort of deal car makers have been afraid of. Volvo will be primarily a contract manufacturer here, and Uber will own the special sauce that makes the vehicle work, and it will own the customer.