Applications of AI in the Automotive Industry
Self-driving cars have become a reality with the adoption of artificial intelligence (AI) technologies in the automotive market. Today, automakers use AI to increase manufacturing and supply chain efficiency and make driving safer and more comfortable.
Importance of Artificial Intelligence in the Automotive Industry
When used in manufacturing and production processes and within automobiles to provide in-car functions, AI technologies offer incredible potential. The automobile industry has six key AI applications, including
Designing and Production
Vehicle manufacturers may improve manufacturing processes, data categorization during risk and vehicle damage evaluation, and many other areas by using machine learning (ML) algorithms and AI-powered solutions. However, some automotive leaders regularly use AI and robotics tools based on NLP, conversational interfaces and computer vision in their manufacturing processes.
Car manufacturers can apply a variety of ML techniques and AI-powered predictive analytics. With technological support, they can assess component requirements and anticipate future demand variations within a reasonable time frame.
Quality Control (QC)
AI can help detect many technical flaws in real-time. An AI system may forewarn a user if a system or component needs repair or replacement depending on data received by sensors within the vehicle. In comparison, AI-powered QC systems detect potential flaws in components before their placement during the production process.
Vehicle manufacturers outfit their automobiles using a variety of AI-powered solutions to ensure the safety and comfort of all passengers while also improving the overall passenger experience. Most systems employ face detection and emotion detection algorithms to assess the states of the passengers and driver. In contrast, others employ NLP and generation technologies to allow passengers to enjoy music or movies and order services and goods while traveling.
Artificial intelligence (AI) has the potential to help improve the wholesome driving experience. For example, AI systems may guide motorists and protect their safety by providing weather and traffic updates, advising the most suitable routes and allowing them to pay for products and services while driving.
AI-powered systems can also assist with insurance claims like drivers can use AI features of the vehicle to collect accident data and fill out claims. Data analytics, NLP, speech recognition, and text processing and creation techniques are necessary for this AI-powered system.
Challenges of Implementing Artificial Intelligence in the Automotive Industry
While the competitive advantages of artificial intelligence in the automotive industry are plenty, there are some restrictions to its practical deployment. Let’s talk about the primary challenges with using artificial intelligence applications in the automotive industry.
Bias Artificial Intelligence
AI systems are prone to bias, so you should address the situation using efficient algorithms rather than an AI system. Ensure all algorithms responsible for safety-critical functions are thoroughly examined and tested. Algorithm correctness can be determined either manually or using sophisticated mathematical correctness assertions.
Collecting a huge dataset of high-quality, well-labeled, and recorded data is a substantial challenge. AI-powered solutions for smart vehicles must be accurate, rapid, and predictable to collect accurate real-time responses to various on-road situations. All data, regardless of sources, must be extensively reviewed and tested to ensure its quality and accuracy.
Limitations of Sensors and Equipment
The technological capabilities of the sensors and data collection devices have a considerable impact on data quality. It is pointless to collect a large amount of relevant data if the data comes from the wrong sensor or equipment.
Explainable AI (XAI)
Explainable AI (XAI) solutions, rather than black boxes, are ideal for the automobile industry. Unlike AI-powered black-box models, XAI systems provide people with transparent and easy-to-grasp decisions. This helps AI engineers spot any systems faults quickly and make appropriate changes.
Non-Adherence with Laws and Regulations
Deep learning and AI applications in the automobile industry are progressing faster than adopting relevant rules and regulations. It leaves some legal loopholes in the development of AI-powered solutions for the automotive manufacturing industry. So, it would help to consider some factors during the software development process.
Using artificial intelligence in the automotive industry offers various advantages. But, despite the competitive advantage of artificial intelligence, its application in the automotive industry is fraught with challenges like algorithm biases and poor data quality. These must be considered while employing artificial intelligence applications in the automotive sector.