The idea of a robot car may seem outlandish and futuristic, but automotive technology has been gradually automating over time. In 1939, General Motors took the first bold step forward with automatic transmission. More recently, GPS tracking became prevalent. The pace of adoption for automation technologies has increased rapidly since the turn of the century, as computers have become more sophisticated and virtual technology has accelerated to allow for more complex processing and integration of data.
In other words, like images in your side mirrors, the self-driving car was always much closer than it appeared. It does not need to be built “from the ground up.” Instead, the automated features in cars that have been developed over the years are being pieced together and combined with new technologies and software to allow for 100% self-driving capabilities.
One important advance in computing technology that will allow these cars to eventually drive without human assistance is Cloud Computing. Speed, efficiency, reliability and affordability all make Cloud Computing the simple and most efficacious choice to host the applications that allow for interaction between cars and elements of the road.
Let’s look at a few of the many pieces of driverless cars and how it’s being used for driverless technology. Then we will explore how Cloud based solutions are playing a part in the broader analysis of data allowing robot cars to be incredibly consistent and safe on the road. eventually clearing the road for widespread consumer use.
From Cruise Control to Adaptive Cruise Control
I personally have a car that does not have cruise control and I consider installing it regularly to avoid speeding tickets. My human error when not looking at the speedometer at all times results in an inconsistent speed.
According to Mental Floss, cruise control was invented by a blind man, Ralph Teetor. He lost his sight when he was only five years old, but he became an accomplished mechanical engineer and a major figure in car history. Teetor both improved automatic transmission and developed the original tool to allow drivers, by and large, to ignore the gas pedal. Originally given the somewhat disturbing name auto-pilot, cruise control was first included as an option by Chrysler in 1958.
Adaptive cruise control (ACC) is the updated version of cruise control that will be a major piece of autonomous driving. Defined by ExtremeTech as “an intelligent form of cruise control that slows down and speeds up automatically to keep pace with the car in front of you,” ACC doesn’t just set the car at your desired speed and continue until you brake. Obviously with standard cruise control, the car would drive you straight into a wall. Adaptive cruise control uses radar sensors to gauge traffic developments and any other obstructions ahead, keeps you positioned in the middle of one lane and gives a lead time of between two and four seconds (your choice).
Typically adaptive cruise control is combined with an accident alert system. With the intelligence of self-driving, accidents will become less likely, but in the current driving climate, accidents are still common. If there is a major deceleration in front, the car hits the brakes and sends you a warning that a crash is imminent.
Vehicle-to-Vehicle, Vehicle-to-Infrastructure and Vehicle-to-Pedestrian
There are three major types of communication between a self-driving car and the environment: vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-pedestrian.
As described by the US Department of Transportation – which has been developing vehicle-to-vehicle (V2V) safety mechanisms in conjunction with car manufacturers since 2002, V2V will allow cars to transmit wireless data back and forth to each other. V2V assesses three primary components of traffic in the vicinity of a car: position, speed, and location (all of these determining the trajectory of the other car in comparison to your own).
Vehicle-to-infrastructure (V2I) allows cars to interact with roadway elements. The Car Connection notes that V2I is not just about safety, as with interaction between a car and a stoplight to know automatically when a red light is on its way. Tests on BMWs in Germany also demonstrated that V2I could help reduce traffic congestion by coordinating the movement of all vehicles. Additionally, it will help with fuel-efficiency: there will be no need to accelerate toward the next light if it is about to change anyway.
Vehicle-to-pedestrian (V2P) is a third component of these interactive technologies (which are together called car-to-X or vehicle-to-X). V2P works by communicating between a car’s computer and a pedestrian’s cell phone. If a road safety application is installed on a pedestrian’s mobile device, both the driver and the pedestrian are alerted with a warning tone whenever their trajectories are becoming dangerously close. This technology is being developed by car makers such as Volvo and Honda.
Big Data and the Role of Cloud Computing in Self-Driving
According to Computerworld, Mark van Rijmenam of BigData-Startups.com believes the typical self-driving car will create 1 GB of data each second. Gartner analyst Thilo Koslowski notes driving data will be uploaded into the Cloud. From there, it can be used by other cars and pedestrian devices, as well as by safety professionals in industry and the government to enhance roadway safety.
Big data is a major trend in business these days, and it is also of great interest to those involved in research. Brian Cameron, executive director of the Center for Enterprise Architecture at Penn State’s College of Information Sciences and Technology (IST), delineates the strong connection between big data and Cloud services. The latter is making analysis of huge amounts of data more possible for every organization.
Simply put, Cloud based services allows computing resources to be provisioned quickly and without the need for expensive infrastructure or management costs. Because “grid computing” allows big data to be organized, understood and manipulated in a cost-effective way, businesses of all sizes can now take advantage of phenomenal computational powers which were previously inaccessible.
The Telegraph notes how Cloud- hosted applications will improve the driving experience in one example situation. When we’re driving today, we might drive past an interstate exit and then run into a traffic jam soon thereafter. With readily available information, you could exit the highway and take a detour around the stopped traffic. Waze already has that idea as a very popular social Cloud based mobile app.
In a self-driving car, all of the information from the sensors of cars will be integrated. Your car will be able to communicate directly with those in its immediate vicinity. Information miles down the road, though, will come from incredibly complex, fast-paced data feeding into algorithms stored in the remote Cloud servers. Autonomous vehicles will be tied into the Internet of things (IoT), allowing them access to vast pools of information stored and processed through Cloud hosted VM’s.
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