"It looks like a palace for Jabba the Hutt."
Does that mean Chicago is a wretched hive of scum and villainy?
They described the five categories of vehicle automation, and explained that the first autonomous (not Musk’s so called “autopilot” which isn’t) vehicles will hit the road in the summer of 2015.
Here's the levels. Most high-functioning systems on the market, like the Tesla version, are in the Level 1-2 range.
No-Automation (Level 0): The driver is in complete and sole control of the primary vehicle controls – brake, steering, throttle, and motive power – at all times.
Function-specific Automation (Level 1): Automation at this level involves one or more specific control functions. Examples include electronic stability control or pre-charged brakes, where the vehicle automatically assists with braking to enable the driver to regain control of the vehicle or stop faster than possible by acting alone.
Combined Function Automation (Level 2): This level involves automation of at least two primary control functions designed to work in unison to relieve the driver of control of those functions. An example of combined functions enabling a Level 2 system is adaptive cruise control in combination with lane centering.
Limited Self-Driving Automation (Level 3): Vehicles at this level of automation enable the driver to cede full control of all safety-critical functions under certain traffic or environmental conditions and in those conditions to rely heavily on the vehicle to monitor for changes in those conditions requiring transition back to driver control. The driver is expected to be available for occasional control, but with sufficiently comfortable transition time. The Google car is an example of limited self-driving automation.
Full Self-Driving Automation (Level 4): The vehicle is designed to perform all safety-critical driving functions and monitor roadway conditions for an entire trip. Such a design anticipates that the driver will provide destination or navigation input, but is not expected to be available for control at any time during the trip. This includes both occupied and unoccupied vehicles.
In many US States, the local Departments of Transportation want nothing to do with enforcement actions. They will let the police/town/etc install red light cameras, but they don't want to be involved beyond that. In fact many red light cameras are operated by private companies under contract with local municipalities.
Here's an example of why DOTs don't want to be involved in enforcement. A while back some politician in New Jersey, not part of the local DOT, floated the idea of using EZPass toll data to automatically issue speeding tickets. This was almost certainly a money grab. Massive amounts of drivers started asking how to get rid of their EZPass accounts and turn in their transponders. DOT knew lower market penetration would negatively impact congestion at toll booths. They, thankfully, squashed the idea quickly.
The main "duty" of most non-tenured professors is to produce research. If you do that best by working regular 9am-5pm hours or by only coming in in the middle of the night, nobody's going to care much. Aside from that, you need to attend occasional meetings and turn your grades in at the end of the semester. Once you have tenure, the obligation to produce continuous research is lessened a bit, and most of the schedule on which you "fulfill your duties" is really up to you.
From my perspective in the trenches, the reduction is not as big as most people might think for CS and the sciences. If you worked like crazy while building your credentials, either for tenure or to a senior position in a non-tenure research track, you can't really slack off too much. You still need to bring in the cash to cover your team, grad student tuitions, and your own salary, which are now more expensive too. This means just as much research effort and proposal writing. This is exacerbated when research funding is cut at a large scale (sequestration). The reduction really comes from i) having established robust lab practices, methods, and management skills and ii) improved proposal writing skills combined with a track record. Junior faculty expend a lot of time finding and developing the right models, processes, and skills.
Another problem is that you spend your early career developing and reinforcing workaholic habits. It is very hard to step away from work, even for a regular weekend. Unlike most high intensity jobs, the flexible time is great for scheduling around family so they actually see you. You can insulate them from the worst of it.
The main advance is the progression towards real-world sensor selection and packaging. If you look at all the cars which completed the Urban Challenge, and the Google cars, you'll notice the spinning Velodyne laser sensor on the roof. It is a great sensor and makes autonomous driving much easier. Unfortunately, that sucker costs more than most luxury cars and would never be deployed the real-world since nobody wants a spinning can on their roof.
Carnegie Mellon would not have won the Urban Challenge without that sensor or the others littered all over the exterior of the car. The major advance for this new Carnegie Mellon car is comparable performance with cheaper sensors fully packaged within the car. This is a big deal since (a) economics limits which sensors you can buy and (b) the car body and shape limit the size and location of sensors. These obviously limit your overall sensing capability.
The new car also has better computer packaging. Most autonomous vehicles have no trunk space and frequently have no back seat room. For a historical perspective, Carnegie Mellon's Navlab 1, which found a spot and parallel parked autonomously in 1992, had racks of computers and an extra air conditioning system to handle the heat load. Urban Challenge vehicles also had racks in their trunk areas. The Cadillac SRX team was able to cram all the computational gear out of sight. This is really Moore's Law, etc but it is still a respectable achievement.
It's not that simple. A lot of groundbreaking work is the result of side project within a larger research effort. Google is a good example of this. The ideas and approach had their origins in the NSF project Larry and Sergey were working on. While the SDLP project probably had an impact on digital libraries, the stated goal of the work, the larger impact was the creation of a technology behemoth with thousands of US jobs and a major influence on the digital economy. Using your model, would Google have happened? Probably not.
Also, the way you posed the question is interesting for other reasons. Whether a person changes their behavior is often based on far more than just basic science and technology advancements. Issues like federal policy (political science) can have a huge impact. For example, I'm working on technology research related to the aging of the population. This is a very real societal need and it is easy to justify the work from a financial perspective (take a look at nursing home and caregiver costs). However, many health and independence technologies are intertwined with privacy, whether Medicare will pay, and other non-technical issues. We rely on the insights of our colleagues doing research in the social sciences to help us understand the interplay between functionality and barriers to acceptance and commercialization. Without their research, we'd probably make very expensive paperweights.
No if you're posting from a vacation destination you're probably happy.
Exactly. If the researchers didn't account for traveling behavior (i.e., check to see if the person was posting from their typical geographical region) then the results would be heavily skewed by vacations. Hawaii, Maine, Nevada, Utah and Vermont are all popular vacation locations.