(Newswire.net — September 30, 2019) — The transportation and auto industry is going through a big change right now. Vehicles are no longer considered just chunks of metal with wheels that move us from place A to B. They are now becoming smart vehicles with all the perks and whistles of the Internet of Things (IoT).
Companies have many ways to tap into this smart car phase with nearshore software outsourcing becoming a great option to jump on this emerging market. They can outsource software engineers in countries around the world, particularly those nearby, Latin America in the case of nearshore, to follow the footsteps of Google, Apple and Tesla.
The big companies such as the aforementioned three all have various smart car designs in play, but are still mostly focused on the U.S. market. There is still a lot of potential in tapping into other markets, such as Asia or South America, or even third world countries that could potentially become lifelong customers.
Lets now examine some of the technologies being developed using machine learning and AI algorithms for the auto industry.
Smart Car Concepts
The smart car is a vehicle fully tapped into the Internet of Things (IoT) in order to access services via a Wifi connection. Examples of smart car technologies include Apple’s CarPlay and Google’s Android Auto. Both of these services allow users to connect their smartphones to their vehicles’ on-screen displays or dashboards with separate interfaces designed for not having to constantly look down onto the screen.
One key feature that drivers can take advantage of using these systems includes navigation with a voiceover explaining traffic surprises ahead of time or turns before they are required. A map is also available showing the vehicle’s location with dynamic traffic conditions, arrival times and mileage traversed during the trip.
Another feature involves drivers having the ability to listen to music and take calls from their dashboard without having to pick up their tethered phone via a USB or similar connection. What makes these systems useful for drivers besides the entertainment factors is how they allow for a safer journey. Not having to look down on a phone or look away from the road can make any drive safer.
Machine learning is a big part of these smart car systems because the system has to tailor itself to different vehicles, driving conditions and drivers. It needs to effectively tell drivers when to turn and where while constantly reading and analyzing traffic conditions, drivers around the vehicle, predict changes in weather and other things of that nature.
This paves the way for developers to tap into other technologies alongside AI for improved auto systems. An example of this is Augmented Reality (AR) head-up displays that will allow drivers to see navigations right on their windows or other driving conditions. This way, they do not even have to look down to see their map. Even windows and windshields may become smart soon and help drivers avoid or predict accidents on the road.
The better and quicker these systems make instant adjustments and warn drivers to change conditions when it comes to things like weather and traffic, the more pleasant a driver’s journey will become. This takes greater advancements in AI and machine learning and development in this regard will continue to improve.
Siri itself, for instance, is backed by machine learning and AI for voice recognition. It will become a part of Apple Carplay come this fall when iOS 13 is released. However, it will be playing catch up to Google as Cortana already offers various ways drivers can interact with their vehicles through the power of their voice.
Smart car software is much more than a single type of app or service. Developers of a wide range of backgrounds can make an influence in this field and take advantage of the latest AI advancements to make the driving experience safer, more fun and tranquil.
Vehicle Internal Systems Are Becoming Computerized
Vehicles today are being designed with computerized systems that gauge the ride and help drivers make better decisions. One example is related to when drivers have to parallel park and back out of tight spaces and the vehicle makes noise to warn drivers that are coming too close with other vehicles or edges.
Many vehicles also come with their own navigation and smart car systems that help drivers reach their destinations quicker and more comfortably. They come with computerized systems gauging everything from repair, cooling to the air conditioning systems. The engine control unit (ECU) is the most powerful computer in most cars. It can be seen as a brain or microprocessor of other processors in play.
Despite that vehicles today use microprocessors or equivalents as well as process data in real-time to monitor coolant or achieve the best possible mileage, they are not really being run with or controlled by AI algorithms controlling these processors yet. We should see advancements in this regard in the future.
Driverless Vehicles
If you do any sort of traveling or even movement in your daily life, you probably use map systems like Google Maps and Apple Maps to find the best routes to your destinations. What makes these maps possible are companies using a combination of satellites and imagery to map them in real-time.
However, Google goes a step further with its autonomous vehicles capturing the images used for its Maps software. These driverless vehicles can be easily spotted in places like Silicon Valley and recognized by their camera system on top of the car rooftops.
This concept of the driverless vehicle within Google started as a tracking system, originally part of Google’s secret X Labs division and is now turning into something much bigger. It is today a part of Google’s Waymo subdivision and its aim is to produce commercial vehicles for taxi-like services. Waymo is a part of Google’s Alphabet division, which is Google’s restructured parent company.
Other companies are also working on driverless car technologies and looking to get into the Uber-like services. Tesla, for instance, has done some work in this regard and Musk has stated that commercial use of driverless vehicles is one of the goals of his company. AI systems are powering driverless vehicles and responsible for their improvements as vehicles are on the road longer and collecting data constantly.
Eventually, there should be a time when people can purchase their own autonomous vehicles for personal use on a daily basis. However, the AI systems and machine learning algorithms have to be worked out for an unprecedented amount of situations vehicles could get into daily. Many of these situations occur randomly or without any way to predict them.
Other roadside conditions, however, are still an issue and can definitely be overcome within a few years or so. These include different-sized rubs or marks on the road the driverless vehicles’ AI systems have to recognize and make sense of.
In New York City, driverless cars are finally making their presence felt, but in a very limited capacity. Six of these vehicles are currently shuttling passengers from a ferry landing at the Brooklyn Navy Yard. This is just a first step to ride-sharing or Taxi-like services being unleashed on larger cities and driven by machine learning-based algorithms rather than human drivers.
Being able to adapt and react naturally to unpredictable events and people’s mistakes like them going into the wrong lane or backing out incorrectly are things that still need work. This is where developers come in handy as their work will pave the way for future advancements and vehicle AI system designs. As more data is collected and analyzed, AI systems will improve and we may see the biggest driving revolution of our era.