Artificial Intelligence (AI) in conjunction with Machine Learning (ML) algorithms is fast replicating and is poised to surpass human decision-making. This is apparent in the automotive industry where it is improving user experience and enabling faster innovation cycles. This is spurred by the enormous data that the AI-enabled solutions can process, access, and help machines take actionable insights. Consequently, it has enabled autonomous driving using technologies like computer vision that help in real-time object recognition.
AI algorithms are enabling better navigation systems, optimizing voice commands, and enhancing predictive fleet maintenance among others. AI and ML are positively impacting automotive manufacturing, improving quality, and enhancing user experience.
Here are some of the many use cases of AI in the automotive industry:
Automotive Manufacturing
AI is integral to all car-making processes at every stage of the production line. AI-powered robotics are picking up parts for assembly from the conveyor belt. Using Deep Learning (DL) algorithms, they learn how to pick, which part to pick, and in which sequence. This is altering the way the workforce is organized in the automotive sector, boosting its level of accuracy, and positively impacting the overall manufacturing process. Sensor data is continually monitored and fed into the AI systems to crunch and improve system performance.
For example,
- KIA Motors has collaborated with Hyundai and developed wearable robots that carry out tedious tasks as they learn more using DL.
- Rethink has its line of robotics that work along with humans on the supply chain and tend to its material and machine handling, carry out tests and package finished products.
Quality Control
Automobile quality control concerns checking all aspects of the vehicle’s body. An otherwise slow and tedious process that is also prone to human errors is being taken over by AI-enabled systems. AI is capable of identifying defects 90%more accurately than a human survey which significantly lowers the chances of problems in the vehicles later on.
In some prominent examples,
- Audi is using ML algorithms to recognize and point out even the slightest mark of crack on its metal sheets.
- Porsche is using AI to test and identify noises that are indicative of issues in the vehicle.
Companies are using the technology to develop systems that will let systems using ML replace the current optical crack detection process. The gathered data will in turn be used to analyze the cause of defects to help improve the production process.
Predictive Maintenance
Predictive analytics systems utilize historical data and Machine Learning techniques to generate insights for future efficacy.
Analytics data gathered from the running fleets are immensely helpful in adding value in terms of determining regular maintenance cycles. Technological advancements such as the ‘Over the Air’ updates are integrated with the vehicle relieving owners of carrying out routine maintenance services. The system creates alerts before critical emergencies take place helping them carry out predictive maintenance as and when necessary.
These also provide necessary data for asset and inventory management, operational planning and costs, and workforce organization.
Emerging data helps the manufacturing units get the complete picture, help build digital twins, and improve future production. ML algorithms are utilized to build prototypes and simulate the models.
Improve Driver Experience
Advanced Driver Assistance Systems (ADAS) is already helping cars auto-lock, handle hands-free phone calls, and auto park them. But, the technology is now participating in gathering insights into the driving habits of the driver, passenger, and the vehicle itself. All such data enable the ADAS to make informed decisions.
A range of AI-powered technologies like computer vision, Natural Language Processing (NLP), and Robotic Process Automation are being used to develop self-driving cars capable of operating without human intervention. Going a step forward, the technology is being used to develop intra-vehicular connectivity where vehicles can exchange data through a central hub and also with other vehicles. This will give them a better understanding of traffic conditions, anticipate routes, and avoid delays and accidents.
Honda, Benz, Ford, and Mazda are developing AI-enabled driver attention alert systems that can recognize driving behavior through steering inputs, and compare acquired data at later stages of the ride. A 360-degree perception technology developed by Waymo can detect vehicles, pedestrians, cyclists, blockades, and obstacles from a distance of around 300 yards and provide insights. AI sensors developed by Nauto are used to reduce its in-fleet drivers’ distractions by assessing driver behavior. It helps minimize collisions and accidents.
Analyzing Road Conditions
AI-powered automotive applications based on the Internet of Things (IoT) are detecting road conditions in real-time to update drivers and provide navigation assistance. Such apps are capable of charting the best driving route based on road conditions like road closures, humps, potholes, or other causes. Drivers get warning signals 100 meters ahead of approaching humps, and potholes, providing a smooth driving experience.
Such apps are an immense help to drivers commuting through congested traffic conditions or those that have to navigate through roads under repair. These AI-powered automotive applications can signal such road conditions ahead of the beginning of the journey. People who are new to the city and unaware of such conditions are sure to find these AI apps immensely helpful when it comes to finding optimized path suggestions. Data combining road conditions and traffic details provide the best possible driving route.
The Road Ahead for Automotive
Over the last few years, experts in the automotive industry have discussed and come up with four major disruptive trends – electrification, autonomous driving, car connectivity with data sensors, and shared mobility, or, a collective concept of information.
These are among the top trends that are anticipated to fuel AI-based uses within the automotive market. The use of AI technologies is improving user experience, enabling faster innovation cycles, enhancing the manufacturing workflow, and streamlining maintenance processes. AI-powered solutions are collecting and processing large chunks of vehicle and external data and analyzing them to build appropriate algorithms. These, in turn, help provide actionable insights and increase privacy and data security.
At Ascentt, we leverage AI technologies ranging from Machine Learning to Natural Language Processing, Computer Vision to Robotic Process Automation, to augment your automotive manufacturing possibilities. Let us meet to discuss the evolving industry and customer needs to attain your maximum business goals.