19 Smart Automobile Use Cases Using Vision AI

In this blog, we’ll delve into the current state of the automotive industry, highlighting the pivotal role that AI and technology play in shaping its future. 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. AI-driven autonomous valet parking systems enable vehicles to independently navigate and park in designated areas within parking facilities. These systems utilize advanced sensors, cameras, and mapping technology to ensure precise positioning.

LeewayHertz has harnessed ZBrain to redefine automotive supply chain management, automating processes and transforming the management of supply chains into a swift and efficient operation. To know how ZBrain transforms supply chain management, dive into the detailed flow for a comprehensive view. Within the finance and accounting departments of the automotive industry, AI is instrumental in automating expense management processes. AI-driven expense management systems can analyze and categorize expense reports, eliminating the need for extensive manual work. Moreover, these systems can employ machine learning to detect potentially fraudulent expenses, enhancing financial control and compliance.

AI For Predictive Maintenance

In the future, AI-based solutions will bring more value, resulting into more advancements in building autonomous driving experiences. Maximize production, accelerate production, and gather data for improved road safety and experience. If we step outside software development, artificial intelligence in automotive manufacturing meets other issues. There are no clear regulations on data collection and governing the new methods of autonomous transportation.

Then, with a great deal of human curation, some blend of popular designs and fresh inspirations based on a company’s stylings emerge. That forms the basis for artists’ 2D hand-drawn sketches that are then recreated as 3D models and clay prototypes. Across the entire auto industry, companies are exploring generative AI to improve vehicle design, engineering, and manufacturing, as well as marketing and sales. Another application of AI for personalized marketing in the industry is through the use of chatbots and virtual assistants.

LabelGPT for Faster Data Annotation in the Automobile Industry

AI-enabled traffic management systems can analyze data to optimize traffic flow and minimize real-time congestion. From suggesting the best and safest routes to coordinating traffic signals with accurate data-driven predictions, AI helps reduce travel time, enhance road capacity, and improve overall transportation management. AI is the powerhouse of various ADAS features, including lane-keeping assist, automatic emergency braking, adaptive cruise control, and parking assist. These systems leverage AI algorithms and sensors to monitor the vehicle’s surroundings, identify potential dangers, and assist drivers in easily collision-free driving, parking, and more. Maintaining the quality of vehicles is the prime concern of an automobile owner to keep a higher customer count. However, inspecting vehicles manually can lead to fewer defect detection, slower issue resolution, and higher turnaround time.

Supply Chain Management and Automation are the one of the most popular applications of AI in the automotive industry. Use cases of artificial intelligence in automotive for digitally are gaining popularity. Digital insurance claiming is one of the top AI use cases that is transforming the automotive industry.

With the help of generative AI solutions, it becomes simple to determine the fraud based on the policyholders behavior patterns. Generative AI works on studying historical data and inputs from vehicle sensors to predict maintenance. It is really useful for fleet mangers and vehicle owners to schedule maintenance, reduce unexpected failures.

With the combination of AI, the Internet can provide a better driving experience to the customers. Our entertainment system can become a proper informant system that will work on drivers’ commands. Instead of just using the car system for listening to music or taking hands free call, a driver can get more technical information about how a car is performing and how much oil is left with just his or her voice. Many companies in different industries were unaware of how AI could provide innovations. But with time, AI started to become a pillar of reaching new levels of innovation. Therefore AI now has a bigger role in providing innovation in automotive industry.

The Top AI Development Company for Automotive AI Solutions: Why Matellio?

Most importantly, voice recognition tools have high personalization capabilities, meaning they can remember the users’ interests and advise adjustments based on their history. The automotive industry heavily relies on manufacturing, where a small error can cause serious problems. But AI technologies can significantly improve the car making process, making it more advanced and efficient.

• This autonomous driving has been simply remarkable for the industry and drivers face less accidents, increase efficiency and perfect for people who cannot drive.
• Any legal analysis, legislative updates or other content and links should not be construed as legal or professional advice or a substitute for such advice.
• Collecting a huge dataset of high-quality, well-labeled, and recorded data is a substantial challenge.
• Similar to deck.gl, AVS allows for visualizing point clouds and bounding boxes, and it also supports real-time playback.

This is why even world-renowned vehicle manufacturers with plenty of resources partner with third-party AI developers to augment their cars with AI. The rapid growth of IoT use cases and advancements in AI, 5G, and cloud computing enable vehicles to connect to each other, mobile devices, and infrastructures, making autonomous driving safer and more efficient. For example, cars can communicate with each other to ensure they keep a safe distance. Another example is connected delivery trucks that can formulate tight convoys at the highest speeds for minimal wind resistance and fuel consumption decrease.

The higher layers combine and interpret these features to identify more complex patterns and objects. This process can be seen in the figure, with more details learnt while proceeding from left to right. Associating an engineering result to a CAD input is a sort of image classification, although a very advanced one. Manufacturing companies have been leveraging generative design to develop more robust and sustainable automotive parts for quite some time. AI in the automobile industry enables productive design systems to create an array of variations for a specific component per the standards defined by designers and engineers.

AI-driven CPQ automation enables car manufacturers to engage in activities like handling contracts and providing rewards & discounts to customers easily. Moreover, AR enhances the purchase journey by providing detailed information about the car’s specifications, features, and available options. It can also simulate real-world scenarios, like how various safety features work or how the vehicle performs in different road conditions. In the past, manufacturers used to check machinery and equipment daily to ensure there was no problem that would lead to failure and these maintenance costs also affected the budget.

Read more about AI For of AI in the Auto Industry here.