Driving cars has become veritably inevitable in modern times. It is as a matter of fact the commonest means of transportation. Whether driving oneself or being driven, everyone enjoys the benefits cars provide. Hauwa Mahmud Kolo writes on a new smart car technology that makes driving even greener.
It is right to say that scientists are really determined to make the word ‘impossible’ non-existent. Here comes a new generation of smarter-car technology that would help drivers and cars manage trips more efficiently, prevent traffic gridlocks and avoid wrecks.
The era of self-governing cars is already upon us. Using relatively simple software and adjustments to existing hardware, major automakers in the United States and Europe are making cars work smarter and greener in a way that has nothing to do with hybrid engines or alternative fuels.
In Nevada, a pretty forward-thinking move was made a couple weeks ago when they passed a measure ordering new regulations for driverless cars. Many vehicles already participate in once-human-driven activities like parking and skid control, and it’s not long until they’ll be able to navigate, make decisions and drive totally by themselves.
Connected to each other and to the cloud, cars will be able to make their own decisions. So the future of driving, put simply, will be largely out of human hands.
Algorithms and analytics will predict driver behaviour and forecast future commutes. Studying the future from a few seconds to several hours down the road, radar-equipped sedans will sense their surroundings, and road trains and car-to-car networks will reduce congestion, preventing fatalities and improving fuel economy.
An engineer at Ford Research and Innovation in Dearborn, Mich, Ryan McGee, says, “A lot of the interactions we have today are to help the driver do their work, but what we are trying to do is help the car, to make the car smart.
“When you take a car and connect it to the cloud, there are so many possibilities. Here are just a few of them:
“Your car will predict where you will drive. If you don’t use mass transit, odds are your drives to and from work or school follow a typical routine. For instance, Ford would like its cars to take advantage of your predictability and guess where you are going when you turn the ignition.
Researchers are feeding driving history into a Google software service called the Prediction API, which uses a machine-learning algorithm to generate a model of predicted behaviour; in this case, a particular driver’s habits.
“The question we ask the model is, ‘Where is this person going to go next?’” says McGee, who is working on the model.
“The model would say, ‘It’s Wednesday at 5:00, so you are pretty likely going to go home’, and it sends that data back to the car.”
The current system connects to the Internet and records where the car is at what time and on what day. The algorithm computes a list of likely trips for that place and time. Based on the trip possibilities, the car can shift its power consumption to run on a battery instead of gasoline, which will be useful for plug-in hybrid cars,” McGee explains.
Ford is already testing it in an Escape SUV plug-in hybrid.
“But all routines can be broken. What if you want a burrito on Wednesday but crave a cheeseburger on Thursday? The car will have no idea where you will likely go come lunchtime. Because of randomness and drivers’ fickle nature, motorists will be able to make additions and corrections to the system if it’s ever installed in mass-market vehicles,” McGee says.
Another function mentioned is that your car will predict what traffic would do.
Once a car knows its driver’s habits, it can incorporate other data from the cloud to make more informed suggestions. If traffic forecasters have enough data and they have good enough models to interpret it, they should be able to tell you at lunchtime what your 5 commute home is going to look like.
“The tool is good at discovering and learning the signature of slowdowns. It will notice at a particular area or especially an interchange, when traffic slows down, then 83 per cent of the time, or whatever it is, you get a much bigger problem.”
Using those signatures, the system analyses real-time data to build a constantly updating model of the traffic situation, changing every five minutes. While existing prediction systems base their forecasts on current conditions, predicting what traffic will look like if nothing changes, the algorithm can recognise and account for the ripple effects of single actions.
Users can log in to the app from a computer to see “journey history”, and can add or delete routes as they choose to control how much information is stored, Day says. He even envisions the app offering coupons for businesses the driver frequents.
“If you have the capability to recognise that somebody drives by a coffee shop so often, that would be valuable information. It has to be used carefully, and managed such that the user has full control over whether they want to have that data shared, but yes, there are certainly capabilities there,” he says.
Another wonderful quality is that your car will predict pedestrians’ and other drivers’ next moves. Now that your car knows what you are going to do, and what the masses are going to do, it needs to know what the car 20 feet ahead is about to do. Car-to-car networks and advanced control algorithms can ensure there are no surprises, and hopefully someday, no accidents. They can also improve engine efficiency and reduce emissions by preventing stop-and-go traffic.
Just last week, the US Department of Transportation announced a pilot programme that will let drivers test future connected car capabilities. Systems will enable cars to communicate with each other and with road infrastructure like traffic lights and railroad crossings, but drivers have to get used to it first. Clinics in a half-dozen cities will let humans test wirelessly connected car technology to see how well we can adapt.
If we don’t adapt well, cars will soon be equipped to deal with it. Researchers at MIT are developing new algorithms that incorporate models of human behaviour to warn drivers of potential collisions and assume control of the car to prevent a crash.
Additional information from www.popularscience.com