By Daryna Pesina, COO @It-Jim

A century ago, the very thought of machines being able to think, make complicated calculations, and come up with effective solutions to pressing problems was more of a figment of science fiction writer’s fantasy rather than a foreseeable reality. Still, as we move into the third decade of the 21st century, we cannot imagine our life without manufacturing robots, marketing and stock trading bots, virtual travel agents, smart assistance, and other things that wouldn’t have come into existence without the achievements in artificial intelligence and machine learning. The role of artificial intelligence and machine learning in the automotive industry is also difficult to underestimate. According to recent reports, the global automotive AI market is poised to grow to $15 billion within the next five years, and for good reason. With AI driving bringing more applications to the automotive industry, more companies decide to deploy AI and machine learning models in the production environment. In today’s article, we’re going to take a closer look at the ways artificial intelligence is transforming the automotive industry and serves its current needs.

A Few Words about Artificial Intelligence

Want to learn more about artificial learning and deep learning in the automotive industry? Let’s first take a closer look at the definitions and main objectives of these branches of computer science. 

Artificial Intelligence science along with its well-known machine learning and deep learning branches pursue concrete goals, which can be inferred from its very name. AI aims to enable machines to carry out the functions and complete the tasks which are normally performed by humans. In essence, AI is a machine with the ability to solve problems hitherto solved by us, humans with our natural intelligence. To evolve to strong AI machines need to learn. When machines can extract meaningful conclusions from large volumes of data sets, they start to demonstrate the ability to learn deeply. Deep learning requires artificial neural networks that operate similarly to biological neural networks in humans. The three technologies now help scientists and analysts interpret tons of data and are hence indispensable for the field of data science. And now it’s high time we discussed the significance of artificial intelligence and data science in the automotive industry.    

Artificial Intelligence and Production of Vehicles

AI is having a large impact upon the automotive sector. We see AI as part of Industry 4.0 initiatives driving up efficiencies in manufacturing plants by improving overall equipment effectiveness, reducing defects, and improving automation on the line. AI is a value-add to data. This means that the manufacturer needs to have a good data environment or a route to a good data environment. Most of the data collection software installed in the last 20 years will have a good set of sensors on them. The collection of data, as well as data science applications in the automotive industry, is extremely important from a holistic point of view. Presently, lots of companies that provide AI services enable automotive businesses to improve their data environment to reach the state where they can leverage AI and realize the value from their data. The automotive sector can also benefit from good AI solutions capable of acting in advance of real time. This can help companies reduce costing, shipment, and robotic weld defects significantly. Automating visual inspection with the help of AI can in turn go a long way in reducing human error in the process and improve traceability. 

Self Driving Cars

When speaking of automotive machine learning projects, it’s impossible not to mention self-driving car solutions. Major technology companies like Lift and Waymo, as well as the automakers like Toyota and General Motors, have spent billions of dollars developing self-driving cars. Autonomous buses and shuttles are currently being deployed in cities and airports, driverless trucks are already delivering goods long distances, and even autonomous flying taxis seem to be our near future. And there’s a good reason for this rapid integration of machine learning in the automotive industry

First of all, self-driving cars will greatly reduce transportation costs for consumers. And by using autonomous fleets of shared electric cars we’d only need ten percent of cars on the road currently, which can help to significantly reduce CO2 emissions. When that shift happens, people will be able to redesign cities and create a safer environment for everyone. The data from the National Highway Traffic Safety Administration indicate that more than 90 percent of car accidents are caused by human error. This means self-driving cars have the potential to save more lives than airbags, seat belts, and stability control combined. 

Although implementing ML in the automobile industry is an expensive technology, there’s definitely room for startups in this space that can create software and collect data needed to scale autonomous vehicles globally. They aim to make these cars safer by gathering data from human drivers. And there’s a big space to combine blockchain technology with fleets of these cars to create even more autonomous systems which Porsche has started trying out to increase the transparency of the decisions made by driverless cars. 

Lots of people wonder how driverless cars can recognize potential threats and react to the environment in real time. Probably, you’ve heard of self-driving cars using neural networks, specific algorithms that power autonomous vehicle perception. Exactly these neural networks enable driverless vehicles to orient themselves on the street and avoid collisions.   

  • Computer Vision

Self-driving vehicles have five core components that help them navigate and maneuver through street traffic. Computer vision is the first step in that pipeline. Whereas humans rely on eyes and brain to handle the steering wheel, whereas out driverless counterparts take advantage of computer vision. Driverless cars use computer images to find lane lines and track other vehicles on the road. The majority of autonomous vehicles utilize lots of cameras to monitor the environment in the most effective way. Tesla, for example, equips its cars with eight surround cameras that provide 360-degree visibility of the area about 490 feet around the car. There are so many tasks that cameras enable, like lane finding, road curvature estimator, obstacle detection, stop sign classification, traffic light detection, and much more. 

  • Sensor Fusion 

Now that we’ve learned so much about computer vision in the automotive industry, it’s about time we took a look at other components. As good as cameras are, there are certain measurements like distance and velocity at which other sensors excel. And some sensors can work better in adverse weather. By combining all other sensor data, we get a better understanding of the world. There are different sensors for different use cases. Thus, radar is good for determining how far away the object and how fast it’s going. Lidar, in its turn, emits an array of laser beans creating a 3D-point cloud and serves as an effective media between a camera and radar. Ultrasonic sensors, on the other hand, have a small sensing distance which makes them useful for lateral movements like parking. 

  • Localization

Localization is how driverless cars figure out what their position in the world is. Our phones are equipped with GPS, so they help us orient ourselves in the unfamiliar terrain. For cars, more sophisticated algorithms are used, though. They help a car localize itself in a given map with the accuracy of 3, 93 inches by matching the point cloud it sees to the point cloud that the map has. 

  • Path Planning

The car charts a trajectory through the world to get to where it wants to go. First, it needs to predict what the other vehicles around it will do to decide which maneuver to take in response to the situation. Lastly, the trajectory is built to execute the maneuver safely. 

  • Control 

Once the car has a trajectory, it has to turn the steering wheel and hit the throttle or brake accordingly to follow that trajectory. When we have an idea of the path we want our cars to follow, we try to control it. At times, controlling a vehicle can be quite tricky, like attempting a hard turn at high speed. This is something race car drivers are good at, and computers now try their best not to fall behind. 

With more industries acknowledging the importance of AI, more self-driving car projects using machine learning are being created on a daily basis. It’s a rare person who would deny the fact that artificial intelligence in car systems is a perfect tool for more than making machines smarter and predicting their failures and malfunctions. Even though challenges still exist, different fields within the automotive industry are already harnessing the potential of the aforementioned techs and seeing increased efficiency and optimization of processes. 

Applications of Artificial Intelligence in Automotive Industry