Are driverless cars steering the future?

Are driverless cars steering the future?

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The automotive industry is one of the most important areas in human lives and we can’t imagine our daily life without our vehicle or some public transport. The technology evolving day-to-day also brings discoveries and trends in the automotive industry. The latest technology introduced in this industry is Artificial Intelligence i.e. AI. It will provide a new era in vehicles. Designing based on AI will give a modern look to the automotive industry.

Introduction of AI to the automotive industry:

Automotive industry is referred to as a business of manufacturing and selling programmed vehicles that involve trucks, passenger cars, farm equipment, and many more commercial vehicles. It also enhances economic growth by employing a large number of people. There is a lot of competition in the automotive market for traditional automakers. The companies who are developing custom software solutions and tech giants easily entered the automotive industry to maximize performance. AI implementation depends upon a system based on data and computing power. Data processing plays a vital role in the empowerment of the working of AI. Companies are applying data science and data engineering technologies to introduce AI in the automotive industry.

Use cases of AI in vehicles and transport: AI use cases are wide in range. AI machine learning car production is expected to rise year by year.  By following use cases step by step the whole system is designed.  In the auto industry machine learning use cases are:

1. Autonomous Driving

It is the most necessary element in the segment of using AI in the automotive industry. Some requirements are common in all segments involving advanced data management, infrastructure integration, and security area. Many companies are delivering a feature named sophisticated adaptive driver assistance systems (ADAS) in self-driving cases and upgrade it to Level 5. There will be a facility for connected devices in the future. Cars will be connecting to our homes and other vehicles also. Manufacturers already put this feature in Audi enables drivers to capture the timing of green waves while driving and avoiding red light.

2. Design and Development

This stage is the beginning of manufacturing a new car and the concept of AI is being implemented first in this stage.  It creates a more innovative design concept that makes it error-free. The main aim is to design the car more compact, maintaining the quality and having space sense.

3. Quality Control

Quality Control such as detecting painted car bodies, minute cracks on metal sheets are found in AI-based machines. When the parts are introduced into production, quality should be maintained as it makes the difference between life and death in a very critical situation. AI features such as object recognition technologies, built-in comparison capabilities and sensor-based objects are available to assess the quality feature.

4. Supply Chain Optimization

In the auto industry, the supply chain process is very complicated due to the massive production of an average vehicle. It is near about 30,000 to 35,000 in number which is supplied by different companies.. With the help of AI, some specific configurations can be built which is used to measure and analyze massive data that can be forecast correctly.

5. Predictive Maintenance

AI can sum up huge data from different sources such as historical maintenance records, sensor data from a vehicle, and weather data. It enables the system to detect problems and block sudden interruption at the time of parts running at their peak.

6. Intelligent Parking Mode

A built-in smart parking mode tells the driver about the absence and presence of appropriate parking space-saving his fuel and time.

Challenges of AI in automotive:

While Implementing AI some challenges are present. The challenging factors are as follows:

  • Fundamentals: When we are eager to start a new project with someone then we have to focus on some fundamental things. If we are prepared to engage with a data partner then it is necessary to make an assurance that the data partner provides end-to-end support with proper guidance.
  •  Complexity: You must find the proper solution to gain objectives while making projects as some complexities will be there.
  • Security: A huge amount of data is available in the automotive industry containing sensitive data that requires security in one place. Data partner offers some security options like onsite-service actions, secure data access, confident crow, etc. to handle the data properly.

CONCLUSION

AI-driven automotive revolution is gradual in speed currently, we hope that more organizations will be leveraged with AI techniques and reliable data in the coming future. These will get more AI automotive projects in the real world.

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