The automobile industry has been ripe for change for many years now. With the advent of new technologies, there are more opportunities to automate processes and improve efficiency. An area seeing lots of development in recent years is artificial intelligence (AI). Artificial intelligence can be used for various tasks in the automotive industry, from improving production line efficiency to developing autonomous vehicles. This blog post will explore how artificial intelligence is used in the automotive industry and what the future holds for this technology.
Hollywood's depictions of artificial intelligence typically feature a group of clever robots determined to exterminate humanity or, at the very least, a rogue overzealous scientist experiment gone wrong. We are secure for the time being since general artificial intelligent sentient machines don't exist yet and aren't likely to anytime soon (don't take my word for it). So safe to say we're a long way from Westworld yet.
However, one must be aware that it is not meant to downplay how AI might affect our future. According to a 2021 Pew Research survey, about 37% of respondents who are less enthusiastic about AI expressed concerns about job loss, privacy, and AI's potential to "surpass human skills." Additionally, tech entrepreneur Elon Musk, a longtime supporter of AI regulation, has said that AI is more hazardous than nuclear weapons. Despite these well-founded worries, we are a long way from being residents of Westworld.
We cannot but admit artificial intelligence (AI) is pervasive and actively involved in our daily lives. AI is working in the background every time we check our favorite social media newsfeed, when we use Google, receive a product or service recommendation, to list a few. Artificial intelligence (AI) has impacted many business sectors, but the manufacturing and automotive sectors have seen the most significant influence. According to projections, the automobile industry's use of AI will expand at a compound annual growth rate of about 40% to $15.9 billion by 2027. The demand for intelligent technologies like speech and image recognition and connected cars is rising steadily around the globe. As a result, autos' design, manufacture, and use will continue to rely on automation and AI.
Applications of artificial intelligence (AI) in the automotive industry
AI technologies have enormous potential when employed in manufacturing, production procedures, and car software to provide in-car services and better user experience. Additionally, factories are becoming more innovative and more effective thanks to AI. Robots with AI are being used in manufacturing facilities to complete activities that are too dangerous or complicated for humans, thus increasing safety while simultaneously accelerating production and cutting expenses. Some applications of artificial intelligence in the automotive industry include;
Production and Design
Artificial intelligence is playing an increasingly important role in the automotive industry. Many car companies are investing in research and development of artificial intelligence technology. This technology is being used to develop new car features and improve manufacturing processes.
Automakers enhance various operations by implementing machine learning (ML) algorithms and AI-driven solutions, including data categorization for risks and vehicle damage appraisal. However, certain leaders in the automotive industry routinely integrate Natural language processing NLP; a field of computer science known as artificial general intelligence that aims to teach computers how to interpret human speech and writing, conversational interfaces, and computer vision techniques into their manufacturing processes.
AI is also becoming a more significant factor in automotive design. Engineers are using artificial intelligence to generate virtual prototypes of new cars that can be tested without the need to construct actual versions, thus expediting the development process and reducing costs.
In the upcoming years, AI will keep revolutionizing the automotive sector. Autonomous vehicle technology will advance further, and artificial intelligence (AI) will be used more frequently in producing cars and other things. The Quadro RTX graphics card from Nvidia is one example of how AI is being used to substantially speed up design procedures. Rethink Robotics creates collaborative robots to do complex jobs like handling bulky materials and inspecting manufactured parts.
Autonomous driving, AKA Self-driving Cars
When the topic of automotive machine learning projects is discussed, you cannot but mention self-driving cars. Autonomous driving is one of the most exciting applications of artificial intelligence in the automotive industry; hence, this section will be copious; stay with me. Car companies are working on developing cars that can drive themselves. The creation of autonomous vehicles is one of the primary uses of AI in the automotive sector. These are vehicles that don't require a human driver to operate. Although this technology is still in its infancy, it has the potential to revolutionize how we travel. Self-driving vehicles could improve accessibility to transportation while lowering accidents, congestion, and pollutants. This technology is still in its early stages, but it has great potential to change the way we live and travel.
AI systems are designed to help drivers and safeguard their safety by alerting them to traffic and weather changes, suggesting the most direct routes, or allowing them to make mobile payments for goods and services. Also, by creating driver assistance programs, autonomous driving, driver risk assessments, and driver monitoring, such as tracking a driver's eye to detect the risk of dozing off behind the wheel, transportation benefits from the usage of AI in the automotive industry.
This also involves the application of Machine learning (ML); Machine learning (ML) is a "branch of artificial intelligence (AI) that provides machines the ability to automatically learn from data and algorithms to identify patterns and make predictions with minimal human intervention"... i.e., leveraging data to improve performance on some set of tasks and gradually improve its accuracy. Although implementing ML in the automobile industry is expensive, 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. There's also a case for blockchain being combined with automobile data to create an autonomous system and make driverless cars more autonomous and less reliant on human input.
Supply chain management: Artificial intelligence can be used to manage supply chains more effectively. This includes things like tracking inventory entries and optimizing delivery routes. AI-powered supply chain management can help.
Ai software
How can driverless cars recognize potential threats and react to the environment in real-time? You may have heard of self-driving cars using neural networks, specific algorithms that power autonomous vehicle perception. It begs the question: How do these neural networks enable driverless vehicles to orient themselves on the street and avoid collisions?
Autonomous vehicles are equipped with sensors and software that enable them to navigate without human input. Major automakers and tech companies invest billions of dollars in developing autonomous vehicle technology. Advanced Driver Assistance Systems (ADAS): ADAS assist drivers with tasks such as parking, lane changing, and collision avoidance. Many new cars now have at least one ADAS feature, such as adaptive cruise control or automatic braking. Vehicle Diagnostics and Maintenance: AI can diagnose vehicle problems and recommend preventive maintenance. For example, Tesla's Model S uses AI to monitor vehicle components and alert owners when something needs to be serviced. Advanced driver assistance systems (ADAS), Vehicle diagnostics and maintenance, Connected and autonomous vehicles (CAVs), and In-vehicle infotainment (IVI) systems are a few activities done by these software. AI software are developed to make better predictions and recommendations. The is to provide decision support and improve operational efficiency. Here are some of the benefits that these programs bring to the automotive industry:
[if !supportLists]➔ [endif]Improved Customer Service: By understanding customer preferences and providing personalized recommendations, AI-powered software can enhance customer service.
[if !supportLists]➔ [endif] Streamlined Inventory Management: Automotive companies can use ai to manage inventory more efficiently and minimize stockouts.
[if !supportLists]➔ [endif]Enhanced Dealer Management: With better lead generation and follow-up capabilities, AI can help dealerships close more sales and improve customer satisfaction.
[if !supportLists]➔ [endif]Optimized Maintenance Scheduling: Automotive companies can use ai to streamline maintenance scheduling and reduce downtime.
[if !supportLists]➔ [endif]Intelligent Traffic Management: AIs can monitor traffic patterns and suggest route optimizations to reduce congestion and save time for drivers.
Furthermore, some key component and activities of artificial intelligence has to be discussed in order to better understand the concept. These mechanical interactions form the process at which self-driving cars function effectively;
Computer Vision
Five essential parts allow self-driving cars to steer and navigate through traffic. The pipeline's first stage is computer vision. Humans use their eyes and brains to control the steering, whereas our driverless counterparts use computer vision. Driverless automobiles employ computer pictures to identify lane markings and follow other vehicles on the road.
Driverless automobiles employ computer pictures
to identify lane markings and follow other vehicles on the road. Most autonomous vehicles need many cameras to scan their surroundings effectively. For instance, Tesla outfits its vehicles with eight surround cameras that offer a 360-degree sight of the space around the vehicle, which is approximately 490 feet. Cameras make it possible to perform a wide range of functions, including lane finding, estimating road curvature, obstacle identification, classifying stop signs, detecting traffic lights, and many more.
Sensor Fusion
Cameras are not the only navigation tool; other sensors are employed to measure things like distance and speed. Additionally, some sensors can perform better in bad weather. There's an in-depth understanding of the world by merging the data from all other sensors. Different sensors are available for various use cases. A radar is also helpful for determining an object's distance and speed. When used as a bridge between a camera and radar, lidar (Light Detection and Ranging) — a remote sensing method that uses eye-safe laser beams to “see” the world in 3D, providing machines and computers an accurate representation of the surveyed environment releases a series of laser pulses that combine to form a 3D point cloud. On the other hand, the small sensing distance of ultrasonic sensors makes them suitable for lateral movements like parking.
Control
To direct or control our vehicle, the steering wheel must be turned, the throttle must be used, and the brakes must be applied appropriately for the car to follow its trajectory. We attempt to control our cars when we have a specific path for them to travel. Driving a car can be challenging, such as turning hard while moving quickly. Computers now try to keep up with race car drivers in this area, where they excel.
A growing number of self-driving automobile projects utilizing machine learning are being developed daily as more sectors recognize the value of AI. Few people would contest the idea that artificial intelligence in automotive systems is an ideal tool for more than just making machines smarter and forecasting their breakdowns and malfunctions. Even though there are still difficulties, certain sectors of the automobile industry are already utilizing the potential of the aforementioned technologies and experiencing improved process efficiency.
Localization:
Localization in this context is the process through which autonomous vehicles determine their location in the world. Our phones' GPS capabilities enable them to assist us in finding our way around unfamiliar surroundings. However, more complex algorithms are utilized for automobiles. By comparing the point cloud that the automobile sees to the point cloud that the map possesses, they enable a car to localize itself within a given map with an accuracy of 3.93 inches.
Final Note
It's evident that artificial intelligence is drastically changing the automobile industry, and it will only become more prevalent in the years to come. AI technology has become a major driving force in the automotive industry (pun intended). So far, two major companies have led in the adoption of AI technology in automotive—Nvidia and Tesla. When it comes to supplying hardware and software standards for developing and utilizing AI models, Nvidia is clearly in the lead. Tesla is continuously using AI, especially for its excessively ambitious autopilot. With so many new opportunities for AI in the automotive sector, it'll be interesting to see how this technology develops and affects the industry. For Advanced AI to recognize everything and everyone equally, it is essential to pay close attention to current localization, data security, algorithm, machine learning, deep learning, and the removal of bias from data. One question for, Have you been keeping up with the latest AI news in the automotive industry?