- Machine learning adoption in the automotive industry
- Machine learning use cases in the automotive market
With self-driving cars under development, the adoption of artificial intelligence and machine learning in the auto industry is well underway. Leading car manufacturers use these technologies in their business processes from design development to the sale of a car. Let's find out what the use cases for artificial intelligence (AI) and machine learning (ML) in the automotive industry are, and what benefits automakers can get by adopting these technologies.
Machine learning adoption in the automotive industry
Artificial intelligence in self-driving cars is the future of the industry, while machine learning in the automotive industry is becoming more common.
- The market for AI in cars will reach $215 billion of annual value by 2025.
- AI machine-learning car installations are expected to rise by 109% by 2025.
- BMW uses artificial intelligence to create autonomous cars that are expected to be available next year.
- Tesla machine learning is used to create a very sophisticated system capable of deep learning to improve its computer vision, predicting, and route planning skills.
- Artificial intelligence and machine-learning self-driving cars are predicted to be here very soon since the recent pandemic has accelerated innovation in the auto industry because of the need for contactless delivery.
Machine learning use cases in the automotive market
Below are the main artificial intelligence and machine learning use cases in the automotive industry. What’s more, we outline the paths you can take to make your car manufacturing business more optimized, customer-centric, and innovative.
Design and development
The use of artificial intelligence begins at the development stage for a new car. At this stage, innovative technologies work together. With the help of augmented and virtual reality, it is possible to create a more thoughtful design concept and eliminate possible errors before they become costly.
The current level of development of artificial intelligence in cars is quite impressive. An intelligent system can suggest thousands of designs for future parts and models, and auto manufacturers can choose the best options. Volkswagen is already using this approach. It is called Generative Design and is based on a specific idea or problem that you need to address for the car’s design like making it more compact without losing quality and a sense of space.
Once the parts are developed, approved, and put into production, it is necessary to carefully control their quality. In the auto industry, the quality of every part is critical as it can make the difference between life and death in a critical situation. Using object recognition technologies as well as built-in comparison capabilities, sensor-based artificial intelligence assesses the quality of every part on the production line. Defective objects are immediately removed. Moreover, the artificial intelligence of Audi recognizes not only defects but also the smallest scratches so that even little things won’t disappoint the future car owner.
Predictive analytics is one of the strongest capabilities of artificial intelligence and machine learning. This is also one of the most promising ways to use machine learning in the automotive industry, which can be implemented in two ways.
- With predictive intelligence, automakers can monitor the health of their equipment. The advantages of this approach are obvious because it allows for the uninterrupted operation of the parts manufacturing plant since all possible problems of maintenance, repair, and replacement of equipment are solved before they arise (reactive maintenance).
- The predictive ability of artificial intelligence for cars can be used to help car owners keep their cars running. For example, the Tesla AI app notifies drivers of the need for technical inspections, oil changes, and other maintenance operations. There are even remote diagnostic capabilities.
The essence of predictive maintenance is that the system analyzes the equipment, compares its specs with industry and safety standards, adds specific information about the operation of the enterprise, and receives a forecast about when a certain part will fail. The essence of reactive maintenance is to prevent this situation and replace a critical part before it crashes the production system. If an unforeseen situation has already happened though, this is a good reason to analyze the prerequisites and find the root cause.
Artificial intelligence and machine learning cannot live without data. These systems can analyze a huge stream of historical and current information, find anomalies and invisible patterns, and draw conclusions about what led to a certain breakdown.
Supply chain optimization
Supply chain optimization is challenging for any business, and auto production is no exception. In the case of the auto industry, its supply chain is extremely complex. This business is highly influenced by political and social factors, it's quite difficult to manage inventory, the cost of raw materials fluctuates, plus low-quality production increases product recalls. Fortunately, all these problems can be solved with AI automotive solutions.
For example, with the help of the Blue Yonder AI and ML project, it becomes possible to optimize an automotive supply chain, plus take into account the fluctuating prices of the resources and adjust the final price accordingly. What’s more, using machine learning and artificial intelligence in self-driving cars to optimize their routes will be one of the future challenges.
Autonomous and electric vehicle optimization
Autonomous and electric vehicles are still in the early stages so there is no established trust in them. To get these technologies adopted worldwide, especially with the pandemic and the need for autonomous delivery, machine-learning startups are focused on making these devices manageable, predictable, and safe. For example, the British project Spark collects driving data to better understand the strengths and weaknesses of the machine-learning-based autonomous vehicles.
Intelligent parking mode
Smart parking is no longer just a dream. It has become a common component of a smart city ecosystem. In response to this trend, car manufacturers are creating cars that have built-in smart parking systems that tell the driver about the presence or absence of free parking places saving him time and fuel.
To do this, automakers need strong systems for analyzing data on city traffic and driver behavior, plus they need to equip the car with sensors and computer vision features.
Marketing for automakers
It would be a shame to miss out on the marketing opportunities that have opened up with artificial intelligence and machine learning in the automotive industry. With its help, auto manufacturers can attract more qualified leads and competently guide them through the sales funnel taking into account the specifics of car sales.
Plus, you can dramatically improve the user experience by combining artificial intelligence for marketing with virtual reality as Porsche has already done. This company invites drivers to test the vehicle in a virtual environment before purchasing. Of course, the data on driver behavior in the application is collected and carefully analyzed to develop marketing strategies.
Convinced that machine learning and artificial intelligence has huge potential for the auto industry? Do you want to implement such a solution for your business? Let's do it together. Share your thoughts with us at firstname.lastname@example.org and we will find a way to solve it using innovative technologies!