AI in Automobile Industry
Automobile manufacturers are continuously exploring methods to improve car quality while speeding up design, development, and manufacturing processes. Instead of just driving them from point A to point B, people want to see vehicles that provide a pleasant, comfortable, and productive experience.
Artificial intelligence (AI) might provide the solution. When used in production and manufacturing processes, as well as within automobiles to provide in-car functions, AI technologies offer immense promise.
According to Gartner, the overall number of new vehicles with autonomy-enabling technology will increase from 137,129 in 2018 to 745,705 in 2023. The worldwide marketplace for autonomous cars is anticipated to achieve $37 billion by 2025.
Let’s look at some examples of how AI and machine learning may be used in the automobile industry:
Design & Manufacturing
Vehicle manufacturers may use AI-powered solutions and machine learning algorithms to optimize production processes, speed up data categorization during risk assessments, and evaluate vehicle damage, among other things. In the automotive and many other industries, Robotics solutions and AI systems based on technologies like natural language processing, computer vision, and conversational interfaces are used very frequently.
Nvidia’s Quadro RTX graphics card, for example, leverages AI to speed up design procedures dramatically. There are companies like Rethink Robotics that create collaborative robots which can handle heavy materials and verify manufactured parts.
Supply Chain
Automobile manufacturers must be able to track every step of a component’s travel and know when to expect it to arrive at the destination factory. As a result, cutting-edge IoT, blockchain, and AI technologies are frequently used in modern supply chains.
Vehicle makers, in particular, may use solutions based on various machine learning algorithms and AI-powered predictive analytics. Manufacturers can evaluate component demand and foresee potential changes in demand with their assistance.
Quality Control
AI can help discover a variety of technological difficulties in real-time. An AI system can warn a user that a given component or system requires maintenance or replacement as soon as the need arises, based on data acquired by in-vehicle sensors. AI-powered quality control systems are also used by manufacturers to detect potential defects in parts before they are installed.
In-car quality control systems primarily rely on data processing and analysis methods, whereas manufacturing solutions make use of AI-based image recognition and sound processing.
BMW employs AI-powered solutions for a variety of operations, including welding tongs predictive maintenance, and paintwork quality inspection. Predii’s AI-powered technology recommends car maintenance based on sensor data analysis.
Passengers’ Perspectives
Manufacturers equip their cars with a variety of AI-powered apps aimed at improving the passenger experience to ensure that all passengers are safe and pleased. To assess the state of the driver and passengers, several systems employ technologies like facial recognition and emotion recognition. Others use natural language processing and generation technologies to allow passengers to watch movies, listen to music, and even place orders for products and services while driving.
Dentsu and Hyundai, for example, funded $10 million in the Audioburst project, which aims to develop an AI-powered infotainment system. Passengers will be able to search music/audio libraries, listen to customized music playlists, and get news updates using this technology, which will include automated voice recognition and natural language comprehension.
Amazon is attempting to make their Alexa speech assistant, which is powered by artificial intelligence, available in a variety of automobiles. Infotainment systems in BMW, Toyota, Ford, and Audi vehicles have already been programmed to work with Alexa.
Assistance to the driver
There are AI systems that help drivers and safeguard their safety by alerting them to traffic and weather changes, suggesting the most efficient routes, and allowing them to pay for products and services while on the road. CarVi is an advanced driving assistance system (ADAS) that analyses traffic data using artificial intelligence (AI). It also warns drivers about potential risks such as poor driving conditions, lane deviation, and forward crashes in real-time. The current best example is Mahindra XUV 700 which has adaptive control. Real-time image and video identification, object detection, and action detection are all used substantially in such solutions, but speech recognition and natural language processing technologies may also be used.
Other systems attempt to take over the driver’s duty, either temporarily, as with Tesla’s autopilot features, or permanently, as with Waymo’s driverless cars and Zoox’s robotic ridesharing vehicles. Complex computer vision skills are frequently combined with real-time data analysis and natural language processing in these systems.
Insurance for automobiles
When it comes to resolving insurance claims, AI-powered solutions offer a lot of promise. In-vehicle AI capabilities may be utilized by the driver to collect incident data and fill out claims. Smart data analytics, speech recognition, natural language processing, and text processing and production would all be required in such a system.
On the insurer’s side, AI systems that make use of image processing and object identification technologies can greatly improve the accuracy of car damage assessments.
Conclusion
Machine learning has a wide range of potential applications in the automobile sector. Manufacturers may use AI technology to develop and create new prototypes, improve supply chain efficiency, and enable predictive maintenance for manufacturing equipment and on-the-road vehicles.
Driver and passenger support services, such as autonomous transportation, in-car shopping and entertainment, rapid insurance claim filing, and so on, are all powered by AI.
However, despite its exciting promise, AI’s usage in the automobile sector is fraught with difficulties. Algorithm biases, data quality, and comprehending how a model arrived at a particular result are among the most significant.
This blog is published as an HA for AI and contribution was also done by :
1) Arshad Patel
2) NISHA MODANI
