19 Smart Automobile Use Cases Using Vision AI

Connected cars are one of the most popular use cases of AI in automotive industry. Connected cars are vehicles that are equipped with an internet connection, enabling them to access a variety of data and information in real-time. This technology provides several benefits to drivers, such as increased safety, improved navigation and convenience, and a range of other services.

• Vehicles can build a multidimensional understanding of their surroundings by integrating data from various sensors such as radar, LiDAR, ultrasonic sensors, and cameras.
• Automobile manufacturers around the world are using AI technology in every aspect of the vehicle manufacturing process.
• Moreover, any event of a collision in autonomous cars can spark questions on liability, seeking the responsible entity for the accident.
• Analyzing driving behavior, AI algorithms carry the potential to accurately assess risk, streamline claims processes, and ultimately lower insurance premiums.
• Furthermore, AI-driven chatbots provide instant assistance, answering queries and guiding customers through various processes like purchasing, scheduling maintenance, or troubleshooting issues.

As we know, Artificial Intelligence (AI) has become more widely available in the automotive industry, and many automobile industries have placed a high priority on safety. Autonomous cars & driving inform drivers about what is going on around them in real-time helping them make the right decisions that prevent accidents. Technologies such as fingerprint readers, facial scanners, iris scans, and voice recognition collect and use biometric data to improve a driver’s in-vehicle experience.

The Journey to Autonomous Vehicles – 3 Questions With Maggie Mhanna, Renault Digital

In the aftermarket segment, AI is a game-changer, offering predictive maintenance solutions that anticipate vehicle issues before they occur. AI-driven insurance models tailor coverage to individual driving behavior, enhancing the overall customer experience. Advancements in chipsets, edge technology, 5G, IoT, and cloud are acting as catalysts. By continuously monitoring the vehicle’s surroundings, AI-driven sensors and cameras can detect and respond to potential hazards in real-time. Whether it’s identifying pedestrians, other vehicles, or obstacles, these systems provide crucial warnings or even intervene by autonomously applying brakes or steering to prevent accidents.

Efficacy tends to stand out in production lines because of the incorporation of AI-controlled bots. These bots collaborate with people in such processes as moving materials, packaging products, and doing tests. It leads to leaner supply chains, reduced equipment failure, fewer quality issues, and ultimately effective production.

Assistance For Drivers

AI technology is already being used with the purpose of assisting drivers to make better decisions while behind the wheel, even helping them avoid certain habits or behaviors. Nonetheless, it is clear that AI is set to revolutionize the automotive industry in a variety of ways. In the automotive design department, implementing AI in the early concept phase (e.g. when C-Levels discuss the launch of a new car) can have a deep impact on the rest of the car’s projects. The positive and beneficial avalanche will be a much wider range of design possibilities from which optimal solutions will be selected at earlier stages than with traditional methods.

When it comes to the industries using AI to improve driving experience and performance, no one can beat Tesla, which sits at the top of the most advanced motor manufacturers list. AI has emerged as a boon for industries; it helps them find the best solutions by offering multiple choices to choose from. General Motors is setting an example again for industries by adopting AI and ML for manufacturing processes.

Natural Language Processing (NLP) for infotainment

Initially introduced 20 years ago, a digital twin is simply a virtual model used for testing processes, products, and services. Analysts, engineers, and scientists are able to study real world scenarios in safe, cost effective, and virtual worlds. The first thing we implemented is what we called “entity detector”, or objects detector, which identifies overlooked things within the vehicle, ranging from pets and bags to infants. This is accomplished through the utilization of an object recognition model from Google’s MediaPipe. Essentially, the entity detector serves as a vigilant assistant, promptly alerting drivers to potential concerns. Our goal with this project was to create a simplified environment to assess the capabilities and viability of AI in the automotive realm, and the results are both interesting and promising.

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