Learning to self-navigate
Making that happen won't be easy. Automakers will need to rely on developments outside the prescribed boundaries of the auto industry -- in universities and at supplier facilities.
The biggest (and possibly most important) participants are the sensor suppliers. Makers of accelerometers and gyroscopes are already working with automotive teams on the development of systems such as dead reckoning, which enables vehicles to know where they are. For dead reckoning, engineers blend information from global positioning satellites (GPS) with inertial data coming from the car's onboard sensors. This gives the vehicle a sense of where it is on a map at any given time. Engineers say that it's necessary to use both sources because GPS systems don't update themselves fast enough to provide exact locations. GPS receives about five signals per second, while inertial sensors may update at rates of 1kHz or 2kHz.
"There's a function called map matching," Muddiman said. "You know all the physical entities -- streets, driveways, intersections -- and you use the inertial data to confirm that you've traversed from one digitized point on the map to another digitized point."
Radar technology, which is already making inroads in adaptive cruise control and collision avoidance, could be employed for forward object detection in autonomous vehicles. (Source: Freescale Semiconductor)
To get the inertial part of that equation, engineers need to draw data from accelerometers and gyroscopes. They typically use so-called low-G accelerometers, which can sense subtle changes in acceleration and direction down to tenths of a G-load. The sensors make it possible for the vehicle to interpret distance and position at high-resolution levels. Even the smallest movement, such as the changing of lanes, can be picked up by the low-G accelerometers. Gyroscopes add to that knowledge by measuring the vehicle's attitude (pitch, roll, and yaw) and thus filling in the data the accelerometers miss.
For engineers, the real trick lies in taking that mountain of data and blending it to form a coherent picture. To do that, they employ processors. Sensors, for example, may contain onboard processors that filter the digital data and send it to a "base band" applications processor, which also examines GPS data. Typically, such computing chores can be handled by dual- or quad-core processors. "The application processor takes all the data and compares it to determine if the information it's getting from the GPS system is accurate," Muddiman said.
Avoiding obstacles
Figuring out a car's location is only one of the chores that makes autonomy possible. A larger, more complex task is determining what's in front of the vehicle and whether it's time to stop or go. To do that, researchers are employing stereo vision cameras, radar systems, and lasers.
Vision, which is not yet playing a big role in autonomous vehicles, uses cameras similar to those employed in smartphones. Engineers say those cameras could provide important information, but researchers haven't figured out how to make sense of it all.
Fastest, easiest implementation will likely be an extension of the diamond lanes used for carpooling. This would somewhat ease the chaotic environment described in the article.
I am quite ready for this technology; reducing accidents while giving more free time is a double win.
However, I do want the human to have final say over the controls. I do not want an AirBus fiasco where the flight computers can override the pilot's instructions.
Thanks, Al. You've hit the nail on the head with your comment about lawsuits. There are a few big problems on the horizon. One is that many drivers won't want to give up control. Another is the legal issues that will arise when machines make mistakes. And the third is that everyone won'y buy their autonomous cars on the same day, or same year, or same decade. There's going to be a mix of human drivers and autonomous cars for awhile, and the machines will need to be able to deal with that.
I can see this as an addition instead of a replacement to how we drive today. This could help sight impaired people become more independent and mobile. It can also solve the drowsy driving issue amoung many truck drivers or long commuters.
No matter how cool, if it doesn't look sexy, it will never catch on...like the segway. Many drivers (aka mistake-prone humans) around the world love speed and versatility. And, they love showing off the skills needed to drive a car well.
Chuck, Excellent article. It makes sense to me that software algorithms are really the key to making this happen. It's not that hard to foresee the hardware being refined and relatively inexpensive but fast, accurate, decision-making is really the key. Especially given the number of lawsuits that could be spawned as a result of product liability issues.
It certainly has admirable goals - I am all for a zero fatality rate in any mode of transportation! But the complexity of successful sensor integration coupled with the challenges of interpreting unpredictable situations overwhelms me. I think doing it in stages is very smart indeed. If the technology is viable - it will certainly solve a lot of problems. I can sympathize with you Naperlou - I have two teenage sons that will soon become new drivers and I find the prospect very worrisome. Completely autonomous driving would not only eliminate the human element - it would also allow those who are uncomfortable driving themselves or who are physically impaired to utilize autonomous driving and be back on the road again...but like Beth, I do have a hard time wrapping my brain around it. I know hubby won't want to give up his 87 Cutlass so I guess they'll have to come up with a refit kit too ;)
Beth, I think it will take time to get used to this. I have trouble riding in the front when my teenage boys drive. At least I can yell at them. I guess I would yell at the car in the future.
On the serious side, I think it is interesting that the current research uses all these complex sensors. Humans use mostly just vision. Multi-sensor fusion, as it is called, is very complex. It might be better to work on vision driven algorithms. If you could merge what humans do with vision with the "concentration" that computer are good at, you would have safe roads.
I have to admit--this is one technology I have a hard time wrapping my brain around, although I know it's only a matter of time before this doesn't seem weird or scary. I think the slow and steady approach to tackling the problem in discrete phases is a necessity. Not only does it ensure everything is working up to snuff, but it gives us, as a society, time to digest and feel comfortable with the whole concept of autonomous driving cars.
As energy efficiency becomes more and more a concern for makers of electronics devices, researchers are coming up with new ways to harvest energy from sound vibration, footsteps, and even electromagnetic fields in the air.
The government wants to study your brain, and DARPA wants to use similar information to give robots true autonomy beyond any artificial intelligence developed to date. Sound like science fiction? It's not.
By refining topologies and using new fluid technology, Moog's new peak sine drive controller increases available power without increasing controller volume.
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