Table of Contents
- The automobile sector is undergoing a massive upheaval. Ambitious manufacturers have begun incorporating sophisticated technology into their products and operations to stay one step ahead of their market competition.
- The Internet of Things (IoT) technology and applications have been integrated into contemporary automobiles.
- Sensors capture vital data on car health and driver behavior. Complex machine learning (ML) algorithms turn this data into meaningful assessments and use it to categorize customers and deliver personalized offers.
- These uses are the most common ones in automobile applications.
AI Use Cases in the Automotive Industry
Automobile manufacturers, parts and software providers, car rental companies, and other automotive-related businesses have benefited from artificial intelligence (AI) and data science applications.
Innovators in the automotive and autonomous driving sectors employ data science and artificial intelligence to create game-changing solutions.
Three far-reaching automotive developments, propelled by machine learning and data science, are described here.
1- Predictive Maintenance:
One of the finest examples of how data science provides value to the automobile industry is predictive maintenance.
- Data analysis has proven to be a successful instrument in the industrial life cycle. To fully realize the potential of data science, it is necessary to analyze and collect data from a wide range of processes across the commercial project lifecycle.
- Artificial intelligence in vehicle manufacturing helps manufacturers cut costs while providing a safer and more efficient manufacturing environment.
- Machine vision methods easily recognize disruptions in automobile components. Machine learning approaches may be used for prototyping and simulation.
- AI can also help forecast the breakdown of automobile components. Smart predictions like these allow industrial processes to run at their most efficient levels, improving productivity in the long run.
Automobile Maintenance Recommendations:
- ML algorithms often deliver repair and maintenance suggestions to drivers. Based on previous occurrences, they can predict when an incident or problem will surface again.
- The data acquired by a vehicle’s sensors, for example, might suggest excessive heat, abrasion, or noise. These faults may also cause a particular car component to fail in the future.
- The machine learning system tracks these circumstances from time to time and assesses the regularity of the instances.
- Based on the data, it also forecasts when the automobile will wear down.
2- Driver Behavior Analytics
AI and Deep Learning-based automobile technologies can deliver various relevant in-car information. Optical and infrared sensors can accurately analyze the driver’s actions and give out warning signals, thus preventing accidents. Some of the key focus areas of driver behavior monitoring include the following:
Rash driving: IoT devices might collect data on vehicle speeds, sharp turns, and sudden stops, among many other issues. Analysis of this information in real-time creates a fair idea of the driver’s driving style.
Driver distraction: Machine learning-based automotive applications can detect driver attentiveness and offer early warning indications.
Driver drowsiness: Automobile applications that use machine learning can detect a driver’s eye gaze and head movements. The software triggers an alert to the driver if they are found to be sleepy.
Driver behavior analytics in the insurance industry:
The insurance industry can analyze driver behavior data to determine consumers’ automobile insurance rates.
Each driver’s risk model is created depending on their driving history, interpersonal issues, health conditions, and other variables that may influence their driving. The premium is calculated using this information.
AI can also accelerate the process of filing insurance claims following an accident.
3- Analyzing Road Conditions
AI-powered car applications can detect construction activities, accidents, speed restrictions, and road closures in real-time so that drivers are aware of them before they begin their journey.
This information is especially useful for motorists who drive on roads where congestion and maintenance work are common occurrences.
These AI applications in the automobile industry have prompted corporations to employ people trained in the discipline. If you want to work in this industry, getting the best artificial intelligence courses online from Great Learning will offer you an advantage over your competition.
It’s hard to discuss automotive machine learning initiatives without referring to self-driving automobile solutions. Automakers like Toyota and General Motors have invested billions of dollars in creating self-driving vehicles.
Although ML implementation in the automotive sector can be costly, there is undoubtedly a place for entrepreneurs in this field to develop software and collect the data required to expand autonomous vehicles internationally. By collecting data from human drivers, they want to make these automobiles safer.
Many people are curious about how self-driving cars can detect possible risks and respond to their surroundings in real-time. You’ve probably heard about self-driving cars that use neural networks, particularly algorithms that help autonomous vehicles perceive their surroundings. These neural networks allow self-driving cars to position themselves on roads appropriately and avoid crashes.
Computer Vision: Self-driving cars have five main components that assist them in navigating and maneuvering through traffic. The initial stage in the process is computer vision. Self-driving cars use computer pictures to locate lane lines and follow other vehicles on the road. A bulk of self-driving cars use many cameras to keep a close eye on their surroundings.
Sensor fusion: Even though cameras are excellent at measures like distance and velocity, other sensors are better. In addition, certain sensors perform exceedingly well in turbulent weather.
Localization: Localization is how self-driving automobiles determine their location in the world. Because our phones have GPS, they can assist us in orienting ourselves in strange territory. However, for automobiles, more advanced algorithms are utilized, and they assist a vehicle in locating itself on a map.
Path planning: The automobile plots a path to go where it needs to go. Selecting which move to execute in reaction to a scenario, the car must first predict what the other vehicles surrounding it will do. Finally, the path is designed to perform the maneuver safely.
Control: Once the automobile has established a route, it must turn the steering wheel and apply the appropriate throttle or brake to maintain that trajectory.
Now is the ideal moment to implement artificial intelligence in automotive applications. Looking at this growing demand for AI and ML specialists in the automobile industry, it’s needless to say that individuals with a strong command over these disciplines have a bright future. If you are willing to master these domains, a wide range of materials is available on the Internet. You can pick the best Great Learning artificial intelligence course online with certificate and start your journey.