As technology evolves, so does the automobile industry with its trends and findings. Recent advances in Artificial Intelligence development have proven that installation technology will soon transform every device we use. As cars were at the top of the list, we decided to look at ways to use Artificial Intelligence in the automotive industry.
In this blog post, we at Oodles, as an AI Development Company, will look at how companies are using data engineering technology and scientific data to transform transportation. We shall take a closer look at the problems that companies are trying to solve, and explore ways to collect data from a variety of sources and build appropriate data pipelines to meet both training and learning needs.
AI Smart Automotive Manufacturing
Manufacturing robots are not new to anyone these days, however, AI applications in automotive manufacturing are not yet widespread. The very high cost is among the top reasons why this powerful technology is only available to market leaders these days. However, high competition in the automotive industry is forcing manufacturers to invest in better equipment and smarter solutions to improve the quality of new releases without compromising time.
Common Use Cases of AI in production use include:
- Extended use of computer vision for malicious detection
- Improved quality control / reduced waste management process
- Predictability retention to increase the efficiency of production equipment
Similar to asset management, high-end automotive manufacturers use robots to collect, move, and filter crafts, as well as to participate in automotive construction. Also, robots are used for outdoor activities, such as car painting and welding. In addition to following a pre-set algorithm, these robots can also detect faults and errors on the surface of the vehicle. The technology that supports intelligent robots is called Simultaneous Localization and Mapping (SLAM) – a method of calculating how to map an unknown location and navigate. The use of robots in an efficient production system can reduce human activity by up to 70%, which will have a positive impact on productivity.
AI Use Cases In Automotive Vehicle
Although you focus on a single industry such as automotive, the number of cases that may use AI is large. NetApp divides AI into the automated industry into four multi-component components for use in each category:
- Private driving
- Connected vehicles
- Travel as a service
- Intelligent production
Naturally, there is a gap between some of these components; success in one place can bring benefits to another. For example, private driving can be an important part of a travel plan as a service. There are also many requirements for all the same components, including infrastructure integration, advanced data management, and security/privacy/compliance.
- AI-powered GPUs for Computer Hardware
The adoption of AI hardware will prove a rapid explosion to enable self-driving technology and upgrade AI algorithms with dedicated AI-enabled GPUs. The growing importance of visual sensors includes high-resolution cameras, LiDAR, and radar in providing precise localization and contextual awareness in internal AI systems enhances the growth of part of the computer hardware. In addition, the development of used AI processors and computer software will enable businesses to design and deploy advanced independent solutions. For example, in September 2019, Horizon Robotics, a leading AI company, launched its second AI processor – Horizon Journey. and high efficiency.
- AI Applies To Telematics Data
Artificial Intelligence and machine learning create great opportunities to read and analyze data from a variety of sources. The remote control uses these types of technologies to provide the next level of solutions for business and user communication tools.
Remote AI is based on our experience creating the Remote Connected Car Platform, which receives data from hundreds of thousands of connected drivers. All data collected on all vehicles – trip, telemetry, RPM engine, acceleration and deceleration, accidents, etc. – should be used to make people’s lives easier, including users and employees in the industry.
That’s why we use artificial intelligence and machine learning to create custom recommendations based on incoming telematics data. AI data processing helps to attract insurance companies and parking and car-sharing services – companies that know how to make money with data – into the ecosystem.
- Driving and User Conduct Monitoring
The performance of AI in private vehicles is not limited to strict requirements, such as safety. AI can be used for more control and entertainment inside the car.
AI helps provide customized entertainment during the trip. Based on the data collected over time, AI can speculate and provide preferences based on user behavior. This could include:
- Seat position adjustment
- Screen adjustment
- Controls air input
- Songs to be played
AI promotes advanced driving so that people can experience easy navigation. Governments, too, have joined the race, urging investors to introduce AI-powered non-motorized vehicles.
- Car Insurance Auto Repair
Both forecasting analytics and computer perspective are cases of use in automotive insurance, each of which has been tested by many AI vendors who sell to the insurance industry and run a business value. Companies like Nexar and TrueMotion use dashboard cameras, smartphone cameras, and IoT sensors to detect when a car is moving.
While operating, cameras are integrated with computer vision detection software that can detect other objects on the road, heavy breaks as a car suddenly stops, and any accidents. All of this can affect insurance deductions or premiums, so videos can be shared with insurance companies so they can adjust the driver’s insurance accordingly.
In addition to installing a vehicle for each vehicle to obtain information about the incident, a mechanical perspective can be used to determine vehicle injuries due to a driving accident. Some solutions allow users to upload images from around their damaged car, and the software can detect the severity of the damage and provide a measure of repair costs.
In these cases, photos and ratings are also shared with the individual insurance agent for approval. Before writing down a customer claim, they can review the results of a computer view and view photos of the actual damage. This will create a faster and more complete process than if the insurer is left to analyze the damage to the vehicle alone.
Their telematics device, called Progressive Snapshot, performed many similar functions before smartphones became commonplace. It is now still part of their Snapshot service in case the customer does not have a smartphone or does not want to connect with Progressive.
- Self-driving Vehicles and AI Driving Assistants
The topic of self-driving car technology can seem to be present throughout the discussion on cases of AI use in the automotive industry. Companies such as NVIDIA, Tesla, and Google Waymo are still working to provide completely independent vehicles, as well as gaining public trust to finally make technology legal throughout the United States.
Most autonomous vehicles use computer vision to detect objects, street lights and pedestrians. However, they use radar waves and LiDAR, a laser distancing system that uses the same terms of operation as radar. This allows the machine to learn the algorithm behind the car to see how far different objects are from the car, such as other cars, pedestrians, and obstacles such as deviating signs or traffic congestion.
Before completely independent driving becomes normal, drivers may become accustomed to AI-assisted driving, which is an application that raises the machine’s vision to find the driver’s job while on the road. Autonomous driving is backed by efficient predictive analytics services that ensure the safety of drivers and can set the most potent value for insurers who want to keep their customers safe and responsible.