In the automotive industry, enhancements made for safety and comfort to your automobile also mean increased opportunities to collect and analyze larger amounts of data than ever before. From smart linked cars to the autonomous vehicles of the near future, having more information plays a role in the development of the modern car. Driverless vehicles, after all, use machine learning Artificial Intelligence. The more you can train your AI, the better it will work. That starts with giving it relevant available data.
Auto manufacturers are seeing an enormous change in how designing a functional, fully-featured car happens from drawing board to test drive. By using data collected by the car companies, designers can feed the AI information on miles driven, simulation data, and test data. Some recent examples of this include Tesla’s Autopilot data, Toyota’s information about autonomous driving simulations, and Uber’s driverless fleet test in Pittsburgh. Companies who share information hope to further their own research, in the hopes of AI incremental learning leaps and faster breakthroughs in safety and technology. In the not-so-distant future, we may see a new Toyota Highlander that can play chauffeur to its driver and passengers.
Big data is defined as the information that would be too large in volume to handle easily with a company’s everyday computer resources. It is massive amounts of data, and how that is processed at a high speed, high volume, and huge variety in the most optimal manner to get the results from a wide-reaching number of places and data inputs. While it is becoming easier all the time to collect and store the data, working with it in high volumes is more difficult. Going without big data is not an option for many organizations. It is the data that helps machine learning progress the most reliably and rapidly. With machine learning, AI learns through the input of data and feedback loops. It is similar to how people learn, although at a much more rapid pace. There are some types of human learning that machines presently cannot grasp, while humans would not be capable of instant memorization of massive databases. The reason AI learning is important is that you want a computer that can think like a human when it comes to the manual operation of your car or truck. Better still if it can think – and react – even faster than a reliable human driver. That means you can use your abstract and other sorts of logic and thought processes to solve work problems or compile grocery lists while riding as a passenger in your own autonomous car.
Big car makers are closing the gap by forming their own data and information divisions to focus on the new roles within companies. For instance, automakers now have data engineers and scientists along with manufacturers and sales teams. With the proper analysis and input of data, AI systems for autonomous cars can learn how to avoid collisions, navigate traffic, and other basic functions of driving in addition to the much more sophisticated and advanced steps a human driver must take in order to operate on the road.
Major manufacturers are taking a proactive approach to harnessing the power of big data in order to build the most advanced vehicles ever imagined. By incorporating teams to analyze the data, car makers are making it easier to get the day-to-day tasks done in addition to the longer-term goals such as AI and autonomous cars. In the area of manufacturing, data can be gathered about all aspects of production and logistics. From the time a part is created to its eventual delivery, a path can be shown that highlights how the part is handled, shipped, and even routed. This allows for improved manufacturing as routes are re-mapped and new and faster alternatives are put into place.
Using this strategy in all areas of the business of being an automaker is a powerful tool for the big names in vehicles, whether it is used to help with marketing, customer relations, accessories, manufacturing, and beyond. An advantage here is that with teams of employees to analyze the data, every part of the process can be scrutinized in detail previously inaccessible, without affecting the current day-to-day operations. It is a bit like applying machine learning to the whole process, except in the analysis aspect it is the humans who are doing the learning.
By: Rick Delgado
Bio: I’ve been blessed to have a successful career and have recently taken a step back to pursue my passion of writing. I love to write about new technologies and how it can help us and our planet.