FEATURES - Endpoints
June 08, 2015

The Future is Autonomous Trains, Planes, and Automobiles


We are living in an age in which the only limitation of technological advancements is time. It is no longer about how we will do something, but simply when we can do it. Robots like R2-D2 and C3PO aren’t in galaxies far, far away anymore, but are just a few decades from us. While robots have captured our imaginations for years, autonomous technologies go beyond our robot companions. Autonomous vehicles, for instance, have been around for a while – unmanned planes have been successful from the 1950s and unmanned ground vehicles have been used in mining and military excursions, starting with the USSR’s Teletank. Now, they have a wider range of capabilities that shape our world daily, from surveillance to infrastructure to retail habits, and more.

How AVs Work
Most autonomous vehicles are equipped with state-of-the-art sensors, which are programmed to map their surroundings, and can sense other moving objects, such as pedestrians on the streets, other moving vehicles, buildings, trees, etc. These sensors work like sonar to map the AV’s surroundings, much like oceanographers map the oceans. This sonar-like capability allows for the creation of extremely detailed 3-D images to navigate through intersections and crossroads. Most autonomous vehicles still cannot read or process road signs, so there is a need for people to first map streets and skies before allowing the AV to drive autonomously.

Types of Autonomous Vehicles
While many connected vehicles possess autonomous assistance features, such as self-parking capabilities, a true autonomous vehicle has yet to be achieved for passenger vehicles. This technology, however, is rapidly developing, as transportation in a globally connected world becomes increasingly more challenging. Currently, the average person spends 4.3 years of their life driving to work to home to the store, etc. This translates to roughly three trips to the moon and back. By utilizing autonomous technologies, we can greatly reduce time spent driving to focus on other tasks, and to reduce traffic as well as traffic and driving-related accidents.

But that doesn’t mean increased safety or efficiencies can’t look good. Audi, for instance, unveiled its new A7 concept vehicle at CES in Las Vegas by driving it up the Strip. The A7 had traveled more than 500 miles from San Francisco to the conference, collecting valuable information that Audi revealed at the show, fully proving the performance potential and style of what connected cars will be like in just a few years. There are many other types of AVs, however, that are already at work, such as surveillance and delivery drones, both heavy and light equipment machines, and more.

Drones
Drones can be defined as UAVs, or unmanned aircraft vehicles, which include the network and personnel on the ground. Usually, drones are associated with unmanned airstrike planes or military surveillance equipment; however, drones are also used for firefighting, disaster relief, search and rescue operations, law enforcement and border patrol, as well as agricultural operations. For instance, drones were used in March of 2013 to locate Devon Davis, a 2-year-old boy who got lost in the Sam Houston Estate swamps. These drones were only implemented after several days worth of traditional search and rescue attempts, and were able to find his body after only three sweeps. While the search ended tragically, if drones had been utilized from the moment authorities had been notified, Davis may have been located much sooner, and found alive. Additionally, at last year’s AT&T Fast Pitch Competition, 13-year-old Kyle Smith introduced his fire tracking system, FireFly, which can be deployed into forests to detect smoke, temperature, wind speed, humidity, and lightning. Another use for drones exists in the retail sector. Many companies, such as Amazon, are looking at integrating drones into their delivery processes. By implementing drones, customers can receive purchases within a matter of hours from warehouses located nearest to them. This can greatly reduce rollouts, employee labor costs, and boost customer satisfaction, as well as increased operations performance and efficiencies.

Yet, despite misconceptions about militarized drones versus public-sector drones, these UAVs seem to be increasing in popularity, as well as versatility. Drones come in all different sizes from the micro or nano drones that are the size of insects to the size of Boeing 737 jets. To operate in U.S. airspace, interested parties must receive a Certificate of Waiver or Authorization from the Unmanned Aircraft System Integration Office, for both civic and public uses, as well as gain approval from state governments. At the end of December 2013, there were more than 500 COAs, but the Federal Aviation Administration is projecting that by 2020, there will be more than 30,000 certified UAVs. This doesn’t include the 50,000-plus hobbyist level, model drones that are currently capable of flight; who knows what that number may be within the next five years.

Light Equipment
But Amazon and other similar distribution companies aren’t just looking into drone UAVs, they are currently utilizing other light equipment AVs, such as forklifts, automated parking assistants, and much more. Amazon in particular is using equipment from the company it acquired (KIVA Systems) in its eighth generation warehouses all across the United States. Each of these warehouses employ this light equipment AV to organize and store items effectively and efficiently. KIVA is said to increase warehouse efficiency as much as four times. Barcodes on the floor guide and direct KIVA all over the warehouse as it uses lasers to pinpoint an object, pick it up, and deliver said item to an employee who will then package and ship it. The Gap, Zappos, and Staples are also utilizing this AV in their own warehouses. KIVA’s success has catalyzed the use of autonomous vehicles in the workplace. It has inspired many new ideas of other self-controlled light machinery, such as MIT’s autonomous forklift that increases safety and ergonomics for workers by moving objects that are too large or heavy.

Heavy Equipment
Heavy equipment is usually categorized by industrial equipment, such as cranes, large haulers, drills, etc. There are many benefits in utilizing autonomous heavy equipment vehicles, which include increased precision and ability to maximize resources, along with increased safety measures. Rio Tinto is an Australian heavy mining equipment company that utilizes AVs in its processes and has seen spectacular results. Because ore and other raw materials reside inside the earth, large equipment must be used to remove and process those resources. The size of this equipment can lead to logistical problems and safety concerns, so Rio Tinto has utilized chips and sensors to help mitigate those challenges. Rio Tinto’s heavy equipment sensors gather more than 4 terabytes of data each day, and have allowed the company to maximize operational efficiencies, increase mining accuracy, and mitigate malfunctions through predictive maintenance costs.

Challenges of AV Implementation
While many companies are already using industry-specific AV technologies, there are several challenges that are preventing widespread adoption, such as connectivity issues, lack of supportive infrastructures, data storage limitations, and cost of adoption.

Connectivity Issues
To sense their surroundings, AVs rely on embedded connectivity devices that monitor location, speed, efficiency, performance, fuel usage, etc. This information is collected and transmitted to an application platform that allows operators to utilize the AV and to ensure that it is operating at peak performance and doing just what it should. However, for AVs that are working in remote locations, maintaining constant connectivity can be difficult. So, a rugged, long-range solution is needed for the AV to remain connected. While many connectivity providers are offering improved connectivity services all the time, we still don’t have universal connectivity.

Lack of Infrastructure
For AVs such as the autonomous car, there is a need for smart cities to become a reality. That involves connecting streetlights, street signs, parking garages, parking meters, and more. However, to implement all of this would require massive restructuring to occur and millions of dollars in sensors, supportive applications, and embedded connectivity devices. Updating current municipalities alone is costly, which is why the application of supportive infrastructure will continue to increase over time.

Data Storage Limitations
Because AVs need to have a multitude of sensors to work properly, it will be imperative to have enough data storage to handle the influx of data. If we were to have autonomous vehicles world wide, there would be no way to manage the incredible amounts of data pouring in.

The Price of Doing Business
Another challenge that the AV industry is facing has to do with the sheer cost of development of devices and platforms, the necessary chips and or sensors, cost of storing and analyzing the data that is collected, and cost of the supportive infrastructure. Getting all of these moving parts in place is going to cost billions of dollars, which won’t happen overnight. Instead, we will have to slowly replace or update existing infrastructure, devices, etc., as needed; eventually, we will be have all of the necessaries in place, do a massive update, and finally reach true automation.

Moving Forward
The autonomous vehicle market could disrupt not only the automotive and heavy and light equipment industries, but could branch out into agriculture, smart grid, smart utilities, retail, and more. Over the next five years, we expect the market to grow by more than 1,000 percent. As more autonomous technologies come into practice, we will be able to mitigate safety concerns, discover new efficiencies, maximize resources (including human labor), and generate multitudes of actionable information.

James Brehm is founder and chief technology evangelist at James Brehm & Associates (www.jbrehm.com).




Edited by Ken Briodagh


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