AI in the Automotive Industry: 6 Key Applications for a Competitive Advantage
The role of AI in today’s automotive industry
Car manufacturers are constantly looking for ways to speed up design, production, and manufacturing processes while improving vehicle quality. Customers want to see vehicles that offer pleasant, comfortable, and productive experiences rather than simply getting them from point A to point B.
Artificial intelligence (AI) may be the answer. AI technologies have enormous potential when applied both in production and manufacturing processes, as well as within vehicles to power in-car functionality.
Gartner predicts that the total number of new vehicles equipped with autonomy-enabling hardware will rise from 137,129 units in 2018 up to 745,705 units by 2023. The size of the global market of autonomous vehicles is expected to reach as high as $37 billion.
6 common AI applications in the automotive industry
The capabilities of AI and ML in the automotive industry: Design and manufacturing
AI-powered solutions and ML algorithms help vehicle manufacturers improve production processes, speed up data classification during risk assessments and vehicle damage evaluations, and do many other things. AI systems and robotics solutions relying on such technologies as computer vision, natural language processing, and conversational interfaces are widely applied in vehicle manufacturing.
Examples
Nvidia’s Quadro RTX graphics card [PDF] uses AI to significantly accelerate design workflows.
Rethink Robotics makes collaborative robots for performing tedious tasks like handling heavy materials and inspecting produced parts.
The capabilities of AI and ML in the automotive industry: Supply chain
It’s vital for vehicle manufacturers to be able to monitor every stage of a component’s journey and know exactly when to expect its arrival at the destination plant. That’s why modern supply chains often rely on cutting-edge IoT, blockchain, and AI technologies.
In particular, vehicle manufacturers can turn to solutions relying on different machine learning algorithms and AI-powered predictive analytics. With their help, manufacturers can estimate demand for components and predict possible changes in demand in a timely manner.
Example
Blue Yonder leverages AI technologies to increase inventory movement visibility and enable manufacturers to predict possible delivery disruptions.
The capabilities of AI and ML in the automotive industry: Quality control
AI can enable timely detection of various technical issues. Based on data gathered by in-vehicle sensors, an AI system can inform a user that a certain component or system requires maintenance or needs to be replaced as early as the need arises. Manufacturers also use AI-powered quality control systems to detect possible flaws in parts before they get installed.
In-car quality control systems mostly rely on data processing and analysis methods, while solutions used in manufacturing leverage image recognition and sound processing AI solutions.
Examples
BMW uses AI-powered solutions for predictive maintenance of welding tongs and paintwork quality analysis, among other tasks.
Predii’s AI-based platform prescribes vehicle repairs based on analysis of sensor data.
The capabilities of AI and ML in the automotive industry: Passenger experience
To make sure all passengers are safe and satisfied, manufacturers enhance their vehicles with all kinds of AI-powered applications meant to upgrade the passenger experience.
Some systems use face recognition and emotion recognition methods to evaluate the state of the driver and passengers. Others deploy natural language processing and natural language generation methods to enable passengers to watch movies, listen to music, and even order goods and services while on the road.
Examples
Dentsu and Hyundai invested $10 million in the Audioburst project to create an AI-powered infotainment system. Using automatic speech recognition and natural language understanding, this system will enable passengers to search music/audio libraries, enjoy personalized music playlists and news briefs, and so on.
Amazon is working on enabling the use of their AI-powered Alexa voice assistant in vehicles of different brands. Integration with Alexa is already available for infotainment systems in BMW, Toyota, Ford, and Audi cars.
The capabilities of AI and ML in the automotive industry: Driver assistance
As for driving experience, AI systems are meant to assist drivers and ensure safety by warning them about traffic and weather changes, offering the most efficient routes, or paying for goods and services on the go.
Examples
CarVi is an advanced driver assistance system (ADAS) that uses AI capabilities to analyze traffic data. It also notifies drivers in real time about possible dangers like driving conditions, lane departure, and forward collisions. Such solutions rely heavily on real-time image and video recognition, object detection, and action detection, but may also use speech recognition and natural language processing technologies.
Other systems aim to take on the driver’s role — either temporarily, as with the autopilot functionalities in some Tesla cars, or completely, as in Waymo’s driverless cars and Zoox’s autonomous vehicles for robotic ridesharing. These systems often combine complex computer vision capabilities with real-time analysis of big data and natural language processing.
The capabilities of AI and ML in the automotive industry: Automotive insurance
AI-powered solutions have great potential in handling insurance claims. On the driver’s side, in-vehicle AI capabilities can be used for gathering incident data and filling out claims. Such a system would need to combine smart data analytics, speech recognition, natural language processing, and text processing and generation.
Examples
On the insurance provider’s side, AI systems leveraging image processing and object detection technologies can be of great help for improving the accuracy of vehicle damage analysis.
The Ping An Auto Owner application uses AI capabilities to assess photos uploaded by users making insurance claims.
Nauto’s intelligent fleet management system has an AI-powered collision detection feature that enables quicker and more accurate processing of insurance claims.
What to consider when building an AI solution for the automotive industry
While AI has promising potential in the automotive industry, the practical adoption of this technology in vehicles has certain limitations. In this section, we overview the key pitfalls to expect when using AI for automotive applications.
Final thoughts
As you can see from our overview, the variety of possible applications of machine learning in the automotive industry are impressive.
But despite its promising potential, the use of AI in the automotive industry is associated with several challenges. Some of the biggest are associated with algorithm biases, data quality, and understanding how a model came to a certain conclusion.
At Apriorit, we have a team of passionate experts who have already created a number of ambitious AI solutions. Get in touch with us trough info@apriorit.com if you need assistance in your next AI project.
Read the full article in our blog: Artificial Intelligence in the Automotive Industry: 6 Key Applications for a Competitive Advantage.
