Data Analytics: A Critical Prerequisite to Artificial Intelligence Mobility

Data analytics is an important prerequisite for artificial intelligence (AI) mobility because it is used to collect and process large amounts of data that are used to train and improve AI systems. Data analytics can be used to identify patterns and trends in data, which can be used to inform the development of AI algorithms and models. Additionally, data analytics can be used to monitor the performance of AI systems in real-world environments and make adjustments as needed. Overall, data analytics plays a critical role in the development and deployment of AI systems, including those used in mobile applications such as autonomous vehicles.

The line between the present and the future is blurred in the automotive industry due to changes in automotive technology. The intelligent self-driving car is no longer a futuristic idea. We will no longer expect the concept of safe transportation, safety, environmental protection, driving pleasure, and convenience, for the "next decade", it would be better to dismiss our fashion in two or three years next. A modern car is not just a machine and wheels, but an inseparable combination of software and hardware. Features such as cruise control, driver assistance, collision avoidance systems, geolocation, and connectivity, among others, have entered the market; to improve vehicle safety, comfort and convenience. However, the company's goal is to make cars an extension of man, not a tool for man. And this is where artificial intelligence (AI) and machine learning (ML) comes into play.

AI seems to have started to run in the car industry. Companies such as Tesla, Ford, Volvo, BMW, Audi, and Mercedes are some of the leaders that have successfully integrated AI and ML into their cars now, aiming to create fully automated cars within 3 years- 5 next. Take Tesla as an example, as one of the pioneers of the electric car industry, its own identity and improved software performance, such as smartphones, are changing the concept of technological production in the region. This company, known for its security features, has joined forces with the business research company, Teradata, to launch a system that takes preventive measures by predicting vehicle equipment failures in the future.

This allows the company to better organize its parts inventory. Lower inventory means lower costs and a more efficient supply chain, which in turn means more satisfied customers. Volvo says that 80-90% of its new cars are "connected" (after customer approval) to collect information based on driving behavior data, vehicle-related license data, customer data, and road reactions way. All of this is to improve the current model and create a future model that interacts seamlessly with consumers.

Volvo Cars uses Teradata and Internet of Things (IoT) analytics solutions to enable advanced analytics for key initiatives such as Volvo Cars Autopilot, Car and Vehicle Communication, and Project 26. For example, sharing traffic situation information, which is collected by many connected Volvo cars, could soon be shared with other cars and traffic officials.

We are witnessing a time when new technology players are becoming important partners for the traditional car industry. For example, US car manufacturer Ford has invested $1 billion in Argo AI to develop self-driving cars. BMW acquired a computer vision company, Mobileye, to put ML in cars in 2021. Audi and Mercedes often make a car now and the time to produce automobiles and create vehicles in the next year.

It's ok to know that the non-sign-up company examines the solution to AI and ML. For example, Google's Service Service, Waymo Relation in Fiat-Chrysler, has been traveling to Phoenix, USA over a year.

Although drivers associated with AIS such as sensagrail and Cartograph, in fully autonomous cars, driving decisions are controlled by AI algorithms that process historical data collected by car manufacturers and real-time data by ML systems ( dynamic learning), which record traffic conditions and place them in real-time situations. driving time after treatment.

Although AI cars cannot understand human thoughts, accurate data and algorithms will help them make our roads a safe and efficient way of transportation. As fully autonomous vehicles are exposed to changing conditions, they will become warehouses of data that must be regularly processed for actionable monitoring and performance improvements. The bigger the data, the more likely it is to have a self-driving car. Therefore, self-driving cars and big data collection will work together to raise the bar for each other.