Generative AI in automotive: How industry leaders drive transformation

Our  initial article on predictive maintenance covered the definition of such a system, its construction, and the key implementation challenges. In this part, we'll delve into how PdM technology is transforming different facets of the automotive industry and its advantages for OEMs, insurers, car rental companies, and vehicle owners.

Best predictive maintenance techniques and where you can use them

In the first part of the article, we discussed the importance of sensors in a PdM system. These sensors are responsible for collecting data from machines and vehicles, and they can measure various variables like temperature, vibration, pressure, or noise. Proper placement of these sensors on the machines and connecting them to IoT solutions, enables the transfer of data to the central repository of the system. After processing the data, we obtain information about specific machines or their parts that are prone to damage or downtime.

The automotive industry can benefit greatly from implementing these top predictive maintenance techniques.

Vibration analysis

 How does it work?

Machinery used in the automotive industry and car components have a specific frequency of vibration. Deviations from this standard pattern can indicate "fatigue" of the material or interference from a third-party component that may affect the machine's operation. The PdM system enables you to detect these anomalies and alert the machine user before a failure occurs.

 What can be detected?

The technique is mainly applied to high-speed rotating equipment. Vibration and oscillation analysis can detect issues such as bent shafts, loose mechanical components, engine problems, misalignment, and worn bearings or shafts.

Infrared thermography analysis

 How does it work?

The technique involves using infrared cameras to detect thermal anomalies. This technology can identify malfunctioning electrical circuits, sensors or components that are emitting excessive heat due to overheating or operating at increased speeds. With this advanced technology, it's possible to anticipate and prevent such faults, and even create heat maps that can be used in predictive models and maintenance of heating systems.

 What can be detected?

Infrared analysis is a versatile and non-invasive method that can be used on a wide scale. It is suitable for individual components, parts, and entire industrial facilities, and can detect rust, delamination, wear, or heat loss on various types of equipment.

Acoustic analysis monitoring

 How does it work?

Machines produce sound waves while operating, and these waves can indicate equipment failure or an approaching critical point. The amplitude and character of these waves are specific to each machine. Even if the sound is too quiet for humans to hear in the initial phase of malfunction, sensors can detect abnormalities and predict when a failure is likely to occur.

 What can be detected?

This PdM technology is relatively cheaper compared to others, but it does have some limitations in terms of usage. It is widely used in the Gas & Oil industry to detect gas and liquid leaks. In the automotive industry, it is commonly used for detecting vacuum leaks, unwanted friction, and stress on machine parts.

Motor circuit analysis

 How does it work?

The technique works through electronic signature analysis (ESA). It involves measuring the supply voltage and operating current of an electronic engine. It allows locating and identifying problems related to the operation of electric engine components.

 What can be detected?

Motor circuit analysis is a powerful tool that helps identify issues related to various components, such as bearings, rotor, clutch, stator winding, or system load irregularities. The main advantage of this technique is its short testing time and convenience for the operator, as it can be carried out in just two minutes while the machine is running.

PdM oil analysis

 How does it work?

An effective method for Predictive Maintenance is to analyze oil samples from equipment without causing any damage. By analyzing the viscosity and size of the sample, along with detecting the presence or absence of third substances such as water, metals, acids or bases, we can obtain valuable information about mechanical damage, erosion or overheating of specific parts.

 What can be detected?

Detecting anomalies early is crucial for hydraulic systems that consist of rotating and lubricating parts, such as pistons in a vehicle engine. By identifying issues promptly, effective solutions can be developed and potential damage to the equipment or a failure can be prevented.

Computer vision

 How does it work?

Computer vision is revolutionizing the automotive industry by leveraging AI-based technology to enhance predictive maintenance processes. It achieves this by analyzing vast datasets, including real-time sensor data and historical performance records, to rapidly predict equipment wear and tear. By identifying patterns, detecting anomalies, and issuing early warnings for potential equipment issues, computer vision enables proactive maintenance scheduling.

 What can be detected?

In the automotive industry, computer vision technology plays a crucial role in detecting equipment wear and tear patterns to predict maintenance requirements. It can also identify manufacturing defects such as scratches or flaws, welding defects in automotive components, part dimensions and volumes to ensure quality control, surface defects related to painting, tire patterns to match with wheels, and objects for robotic guidance and automation.

Who and how can benefit from predictive maintenance

Smart maintenance systems analyze multiple variables and provide a comprehensive overview, which can benefit several stakeholders in the automotive industry. These stakeholders range from vehicle manufacturing factories and the supply chain to service and dealerships, rental companies, insurance companies, and drivers.

Below, we have outlined the primary benefits that these stakeholders can enjoy. In the OEMs section, we have provided examples of specific implementations and case studies from the market.

Car rentals

 Fleet health monitoring and better prediction of the service time

Managing service and repairs for a large number of vehicles can be costly and time-consuming for rental companies. When vehicles break down or are out of service while in the possession of customers, it can negatively impact the company’s revenue. To prevent this, car rental companies need constant insight into the condition of their vehicles and the ability to predict necessary maintenance. This allows them to manage their service plan more efficiently and minimize the risk of vehicle failure while on the road.

Car dealerships

 Reducing breakdown scenarios

Car dealerships use predictive maintenance primarily to anticipate mechanical issues before they develop into serious problems. This approach helps in ensuring that vehicles sold or serviced by them are in optimal condition, which aids in preventing breakdowns or major faults for the customer down the line. By analyzing data from the vehicle's onboard sensors and historical maintenance records, dealerships can identify patterns that signify potential future failures. Predictive maintenance also benefits dealerships by allowing for proactive communication with vehicle owners, reducing breakdown scenarios, and enhancing customer satisfaction

Vehicle owners

 Peace of mind

Periodic maintenance recommendations for vehicles are traditionally based on analyzing historical data from a large population of vehicle owners. However, each vehicle is used differently and could benefit from a tailored maintenance approach. Vehicles with high mileage or heavy usage should undergo more frequent oil changes than those that are used less frequently. By monitoring the actual vehicle condition and wear, owners can ensure that their vehicles are always at 100% and can better manage and plan for maintenance expenses.

Insurance companies

 Risk & fraud

By using data from smart maintenance systems, insurance companies can enhance their risk modeling. The analysis of this data allows insurers to identify the assets that are at higher risk of requiring maintenance or replacement and adjust their premiums accordingly. In addition, smart maintenance systems can detect any instances of tampering with the equipment or negligence in maintenance. This can aid insurers in recognizing fraudulent claims.

OEMs successful development of PdM systems

BMW Group case study

The German brand implements various predictive maintenance tools and technologies, such as sensors, data analytics, and artificial intelligence, to prevent production downtime, promote sustainability, and ensure efficient resource utilization in its global manufacturing network. These innovative, cloud-based solutions are playing a vital role in enhancing their manufacturing processes and improving overall productivity.

The BMW Group's approach involves:

•     Forecasting phenomena and anomalies using a cloud-based platform.    Individual software modules within the platform can be easily switched on and off if necessary to instantly adapt to changing requirements. The high degree of standardization between individual components allows the system to be globally accessible. Moreover, it is highly scalable and allows new application scenarios to be easily implemented.
•     Optimizing component replacements       (this uses advanced real-time data analytics).  
•     Carrying out maintenance and service work in line with the requirements of the actual status of the system.  
•     Anomaly detection using advanced AI predictive algorithms.  

Meanwhile, it should be taken into account that in BMW's body and paint shop alone, welding guns perform some 15,000 spot welds per day. At the BMW Group's plant in Regensburg, the conveyor systems' control units run 24/7. So any downtime is a huge loss.

→  SOURCE case study.

FORD case study

Predictive vehicle maintenance is one of the benefits offered to drivers and automotive service providers as part of Ford's partnerships with CARUSO and HIGH MOBILITY.  In late 2020, Ford announced two new connected car agreements to potentially enable vehicle owners to benefit from  a personalized third-party offer.

CARUSO and HIGH MOBILITY will function as an  online data platform that is completely independent of Ford and allows  third-party service providers secure and compliant access to vehicle-generated data. This access will, in turn, enable third-party providers to create personalized services for Ford vehicle owners. This will enable drivers to benefit from smarter  insurance, technical maintenance and roadside recovery.

Sharing vehicle data (warning codes, GPS location, etc.) via an  open platform is expected to be a way to maintain competitiveness in the connected mobility market.

→  SOURCE case study.

Predictive maintenance is the future of the automotive market

An effective PdM system means less time spent on equipment maintenance, saving on spare parts, eliminating unplanned downtime and improved management of company resources. And with that comes more efficient production and customers’ and employees’ satisfaction.

As the data shows, organizations that have implemented a PdM system report an average  decrease of 55% in unplanned equipment failures.  Another upside is that, compared to other connected car systems (such as infotainment systems), PdM is relatively easy to monetize. Data here can remain anonymous, and all parties involved in the production and operation of the vehicle reap the benefits.

Organizations have come to recognize the hefty returns on investment provided by predictive maintenance solutions and have thus adopted it on a global scale. According to Market Research Future, the global Predictive Maintenance market is projected to grow to  111.30 billion by 2030 , suggesting that further growth is possible in the future.

 Ever since Henry Ford implemented the first production line and launched mass production of the Ford Model T, the automotive industry has been on the constant lookout for ways to boost performance. This aspect has become even more relevant today, given the constant market and social unrest. Coming to rescue supply chain management and product lifecycle optimization is predictive maintenance. Not only OEMs, but the entire automotive industry: insurers, car rental companies and vehicle owners are benefiting from the implementation of this technology.

Predictive maintenance explained

Predictive maintenance is an advanced maintenance approach that utilizes data science and predictive analytics to anticipate when equipment or machinery requires maintenance before it faces a breakdown.

The primary aim is to schedule maintenance at optimal times, considering convenience and cost-effectiveness while maximizing the equipment's longevity. By identifying potential issues before they become critical, predictive maintenance significantly reduces the likelihood of equipment breakdowns.

Various types of maintenance strategies are employed in different industries:

1.     Reactive Maintenance:    Also known as "run-to-failure," this method involves waiting for equipment to fail before conducting maintenance. Therefore, unscheduled downtime and higher repair costs may occur.
2.     Periodic Maintenance    : This approach entails performing maintenance tasks at regular intervals, regardless of the equipment's condition. It helps prevent unexpected breakdowns but may lead to unnecessary maintenance if done too frequently.
3.     Smart Maintenance    : Smart maintenance utilizes advanced technologies like IoT devices and sensors to monitor equipment in real-time and identify anomalies or potential failures.
4.     Condition-Based Maintenance    : This strategy relies on monitoring the equipment's condition while it is in operation. Maintenance is only carried out when data indicates a decline in performance or a deviation from normal parameters, optimizing maintenance schedules and reducing unnecessary work.
5.     Predictive Maintenance    : The most advanced type of maintenance uses real-time operational data and predictive analytics to forecast when maintenance is required. It aims to schedule maintenance before equipment failure occurs based on data-driven predictions, thus minimizing downtime, reducing costs, and prolonging equipment lifespan.

Predictive maintenance employs various techniques, such as vibration analysis, acoustic monitoring, infrared technology, oil analysis, and motor circuit analysis. These methods enable continuous equipment condition monitoring and early detection of potential failures, facilitating timely maintenance interventions.

Differentiation between predictive maintenance and preventive maintenance

 Predictive maintenance hinges on the real-time condition of assets and is implemented only when the need arises. Its purpose is to anticipate potential failures by monitoring assets while they are actively operational. Unlike  preventive maintenance , this approach is rooted in the current operational state of an asset rather than statistical analysis and predetermined schedules.

Essential steps in creating a predictive maintenance solution

Predictive maintenance solutions utilize a combination of sensors, artificial intelligence, and data science to optimize equipment maintenance.

The development of such solutions varies depending on equipment, environment, process, and organization, leading to diverse perspectives and technologies guiding their creation. However, there are steps common to every project: data collection and analysis, model development and deployment, as well as continuous improvement.

Here is a step-by-step process of how solutions are developed in the  automotive industry :

•     Data Collection    : Relevant data is collected from sensors, equipment logs, vehicle diagnostics, telemetry, and other sources. This data includes information about the performance, condition, and behavior of the vehicles, such as engine temperature, fuel consumption, mileage, and more. Telematics systems can provide real-time data on vehicle location, speed, and usage patterns, while maintenance logs record historical maintenance activities, repairs, and part replacements.

•     Data Preprocessing    : The collected data is organized, and prepared for analysis. Data preprocessing involves cleaning the data by removing outliers or erroneous values, handling missing values through imputation or interpolation, and converting the data into a suitable format for analysis.

•     Feature Engineering    : Important features or variables that can provide insights into the health and performance of the vehicles are selected from the collected data. These features can include engine vibration, temperature, fuel consumption, mileage, and more. Feature selection step involves identifying the most relevant features that have a strong correlation with the target variable (e.g., equipment failure). It helps to reduce the dimensionality of the data and improve the model's efficiency and interpretability. Later, selected features are transformed to make them more suitable for modelling. The process may include techniques such as logarithmic or exponential transformations, scaling, or encoding categorical variables.

•     Model Development    : Machine learning algorithms are applied to the selected features to develop predictive models. These models learn from historical data and identify patterns and relationships between various factors and equipment failures. The algorithms used can include regression, decision trees, random forests, neural networks, and more.

•     Model Training and Validation    : The developed models are trained using historical data and validated to ensure their accuracy and performance. This involves splitting the data into training and testing sets, evaluating the model's performance metrics, and fine-tuning the model if necessary.

•     Deployment and Monitoring    : The trained models are deployed into the predictive maintenance system, which continuously monitors real-time data from sensors and other sources. Telematics systems are used to collect GPS and vehicle-specific data, which it transmits through different methods (cellular network, satellite communication, 4G mobile data, GPRS) to the central server. The system detects anomalies, recognizes patterns, and provides insights into the health of the vehicles. It can alert maintenance teams when potential issues are detected.

•     Continuous Improvement    : The predictive maintenance solution is continuously improved by collecting feedback, monitoring its performance, and updating the models and algorithms as new data becomes available.

Most common problems in deploying predictive maintenance solutions

Implementing predictive maintenance solutions in a fleet of vehicles or in a vehicle factory is a process that requires time, consistency and prior testing. Among the main challenges of rolling out this technology, the following aspects in particular are noteworthy.

Data integration

Integrating data from many sources is a significant barrier to implementing predictive maintenance solutions. To accomplish this with a minimum delay and maximum security, it is necessary to streamline the transfer of data from machines to ERP systems. To collect, store, and analyze data from many sources, businesses must have the proper infrastructure in place.

Insufficient data

Lack of data is a major hindrance to implementing predictive maintenance systems. Large amounts of information are needed to develop reliable models for predictive maintenance. Inadequate information might result in inaccurate models, which in turn can cause costly consequences like premature equipment breakdowns or maintenance.

To get over this difficulty, businesses should collect plenty of data for use in developing reliable models. They should also check that the data is relevant to the monitored machinery and of high quality. Businesses can utilize digital twins, or digital representations of physical assets, to mimic the operation of machinery and collect data for use in predictive maintenance systems.

Process complexity

Transitioning from preventive to predictive maintenance is complex and time-intensive. It requires comprehensive steps beyond technology, including assembling a skilled team and managing upfront costs. Without qualified experts versed in software and process intricacies, project success is doubtful.

High costs

The implementation of predictive maintenance programs comes with substantial costs. These upfront expenses pose challenges, including the need to invest in specialized sensors for data collection, procure effective data analysis tools capable of managing complexity, and possibly hire or train personnel with technical expertise.

To address these hurdles, collaboration with specialized vendors and the utilization of cloud-based solutions can prove cost-effective. Additionally, digital twin technology offers a way to simulate equipment behavior and minimize reliance on physical sensors, potentially reducing overall expenses.

Privacy and security issues

The implementation of predictive maintenance involves extensive data collection and analysis, which can give rise to privacy concerns. Companies must adhere to applicable data protection laws and regulations, and establish proper protocols to safeguard the privacy of both customers and employees. Even though predictive maintenance data may be anonymized and not directly linked to specific individuals, it still necessitates robust security measures, since preventing data breaches and unauthorized access to vital company information is crucial for overall success.

What Are the Benefits of Predictive Maintenance?

Life cycle optimization, stock management, or even recycling management - in each of these fields predictive maintenance can bring substantial benefits. And this is not only for OEMs but also for fleet operators, transportation or logistics companies. And even for the end user.

Below we list the key benefits of implementing  predictive maintenance in an automotive-related company:

•     Extended lifespan:    Predictive maintenance technology detects early signs of wear and potential malfunctions in-vehicle components such as engines, transmissions, and brakes. By addressing these issues proactively, vehicles experience fewer major breakdowns and continue to operate efficiently over a longer period.

•     Cost savings:    By addressing issues at an early stage, automotive companies can avoid expensive breakdowns and prevent further damage. This proactive approach not only reduces the need for costly replacement parts but also minimizes the labor and operational costs associated with major repairs, resulting in significant long-term cost savings.

•     Minimized downtime    : Through continuous monitoring and analysis, predictive maintenance predicts when maintenance or repairs are needed and schedules them during planned downtime. This minimizes the likelihood of unexpected breakdowns that can disrupt operations and lead to extended periods of vehicle inactivity. By strategically timing maintenance activities, vehicles spend more time on the road.

•     Increased efficiency    : Any iissues are detected early, enabling timely corrective actions. This proactive approach leads to improved fuel economy, reduced emissions, and overall enhanced efficiency. Vehicles operate at their peak performance, contributing to a more sustainable and environmentally friendly fleet.

•     Enhanced security:    Constant monitoring for abnormal vibrations, temperature variations, and fluid leaks ensures that potential issues compromising vehicle safety and security are detected promptly. By addressing these concerns before they escalate, predictive maintenance contributes to ensuring the security of both the vehicle and its occupants. This feature is particularly valuable in critical applications where reliable vehicle performance is paramount, such as emergency response scenarios.

•     Avoiding over-maintenance    : If you over-maintain corporate resources, it can have the same negative consequences as when failing to maintain them on time. With predictive maintenance, you can focus on maintaining crucial resources at the best possible time and with the best possible results.

•     Compliance with required standards and regulations    : Laws and regulations related to vehicle production are constantly evolving and pushing OEMs to make numerous production changes (e.g. the legislation related to EV production). Predictive maintenance allows you to better suit the new expectations of legislators and monitor the points of production that are most dependent on the legal context.  
  
•     Easier management of parts and materials    : As connected cars diagnostic systems become more sophisticated, drivers have the option to make small repairs sooner and keep their vehicles in a better condition. All this means that OEMs and licensed repair shops need fewer parts and can better manage supply chains.

 Predictive maintenance clearly is not a one-size-fits-all solution for all sectors. Notably, it will work well for high production volumes and short lead times and anywhere you need to ensure reliability, security and convenience.

The automotive industry is a perfect fit for this model. As shown in the examples featured in the second part of the article, the top players in the market are tapping into this technology.

According to  Techsci Research , “  The global predictive maintenance market was valued at USD 4.270 billion in 2020 and is projected to grow around USD 22.429 billion by 2026”.

 Just a few years ago, artificial intelligence stirred our imagination via the voice of Arnold Schwarzenegger from "Terminator" or agent Smith from "The Matrix". It wasn't long before the rebellious robots' film dialogue replaced the actual chats we have with Siri or Alexa over our morning cup of coffee. Nowadays, artificial intelligence is more and more boldly entering new areas of our lives. The automotive industry is one of those that are predicted to speed up in the coming years. By 2030, 95-98% of new vehicles are likely to use this technology.

    What will you learn from this article?  

•     How to use AI in the production process  
•     How AI helps drivers to drive safely and comfortably  
•     How to use AI in vehicle servicing  
•     What companies from the AI ​​industry should pay attention to if they want to introduce such innovations  
•     You will learn about interesting use cases of the major brands  

Looking at the application of AI in various industries, we can name five stages of implementation of such solutions. Today, companies from the Communication Technology (ICT) and Financial Services ("Matured Industries") sectors are taking the lead. Healthcare, Retail, Life Science ("Aspirational Industries") are following closely behind. Food & Beverages and Agriculture ("Strugglers") and companies from the Chemicals and Oil and Gas sectors ("Beginners") are bringing up the rear. The middle of the bunch is the domain of  Automotive and, partly related to it, Industrial Machinery.

Although these days we choose a car mainly for its engine or design, it is estimated that over the next ten years, its software will be an equally significant factor that will impact our purchasing decision.

AI will not only change the way we use our vehicles, but also how we select, design, and manufacture them. Even now, leading brands avail of this type of technology at every stage of the product life cycle - from production through use, to maintenance and aftermarket.

Let's have a closer look at  the benefits a vehicle manufacturing company can get when implementing AI in its operations.

Manufacturing - how AI improves production

1. You will be able to work out complex operations and streamline supply chains

An average passenger car consists of around 30,000 separate parts, which interestingly enough, are usually ordered from various manufacturers in different regions of the world. If, on top of that,  we add a complicated manufacturing process, increasingly difficult access to skilled workers and market dependencies, it becomes clear that potential delays or problems in the supply chain result in companies losing millions. Artificial intelligence can predict these complex interactions, automate processes, and prevent possible failures and mishaps

•  Artificial intelligence complements     Audi's    supply chain monitoring. When awarding contracts, it is verified that the partners meet the requirements set out in the company's internal quality code. In 2020, over 13,000 suppliers provided the Volkswagen Group with a self-assessment of their own sustainability performance. Audi only works with companies that successfully pass this audit.

2. More efficient production due to intelligent co-robots working with people

For years, companies from the automotive industry have been trying to find ways to enhance work on the production line and increase efficiency in areas where people would get tired easily or be exposed to danger. Industrial robots have been present in car factories for a long time, but only artificial intelligence has allowed us to introduce a new generation of devices and their work in direct contact with people. AI-controlled co-bots move materials, perform tests, and package products making production much more effective.

•     Hyundai Vest Exoskeleton (H-VEX)    became a part of Kia Motors’ manufacturing process in 2018. It provides wearable robots for assembly lines. AI in this example helps in the overall production while sensing the work of human employees and adjusting their motions to help them avoid injuries.

•     AVGs (Automated Guided Vehicles)    can move materials around plants by themselves. They can identify objects in their path and adjust their route. In 2018, an OTTO Motors device carried a load of 750 kilograms in this way!

3. Quality control acquires a completely new quality

The power of artificial intelligence lies not only in analyzing huge amounts of data but also in the ability to learn and draw conclusions. This fact can be used by finding weak points in production, controlling the quality of car bodies, metal or painted surfaces, and also by monitoring machine overload and predicting possible failures. In this way, companies can prevent defective products from leaving the factories and avoid possible production downtime.

•     Audi    uses computer vision to find small cracks in the sheet metal in the vehicles. Thus, even at the production stage, it reduces the risk of damaged parts leaving the factory.
•     Porsche    has developed "Sounce", a digital assistant,  using deep learning methods. AI is capable of reliably and accurately detecting noise, for example during endurance tests. This solution, in particular, takes the burden off development engineers who so far had to be present during such tests.  Acoustic testing based on Artificial Intelligence (AI) increases quality and reduces production costs.

4. AI will configure your dream vehicle

In a competitive and excessively abundant market, selling vehicles is very difficult. Brands are constantly competing in services and technologies that are to provide buyers with new experiences and facilitate the purchasing process. Manufacturers use artificial intelligence services not only at the stage of prototyping and modeling vehicles, but also at the end of the manufacturing process, when the vehicle is eventually sold. A well-designed configurator based on AI algorithms is often the final argument, by which the customer is convinced to buy their dream vehicle. Especially when we are talking about luxury cars.

•     The Porsche Car Configurator    is nothing more than a recommendation engine powered by artificial intelligence. The luxury car manufacturer created it to allow customers to choose a vehicle from billions of possible options. The configurator works using several million data and over 270 machine learning modules. Effect? The customer chooses the vehicle of their dreams based on customised recommendations.

Transportation - how AI facilitates driving vehicles

5. Artificial intelligence will provide assistance in an emergency

A dangerous situation on the road, vehicle in the blind spot, power steering on a slippery surface. All those situations can be supported by artificial intelligence, which will calculate the appropriate driving parameters or correct the way the driver behaves on the road. Instead of making automatic decisions - which are often emotion-imbued or lack experience - brands increasingly hand them over to machines, thus reducing the number of accidents and protecting people's lives.

•     Verizon Connect    solutions for fleet management allow you to send speed prompts to your drivers as soon as your vehicle's wipers are turned on. This lets the driver know that they have to slow down due to adverse road conditions such as rain or snow. And the intelligent video recorder will help you understand the context of the accident - for instance, by informing you that the driver accelerated rapidly before the collision.

6. Driver monitoring and risk assessment increase driving safety and comfort

Car journeys may be exhausting. But not for artificial intelligence. The biggest brands are increasingly equipping vehicles with solutions aimed at monitoring fatigue and driver reaction time. By combining intelligent software with appropriate sensors, the manufacturer can fit the car with features that will significantly reduce the number of accidents on the road and discomfort from driving in difficult conditions.

•     Tesla    monitors the driver's eyes, thus checking the driver's level of fatigue and preventing them from falling asleep behind the wheel. It’s mainly used for the Autopilot system to prevent driver from taking short nap during travel.
•     The BMW 3 Series    is equipped with a personal assistant, the purpose of which is to improve driving safety and comfort. Are you tired of the journey? Ask for the "the vitalization program" that will brighten the interior, lower the temperature or select the right music. Are you cold? All you have to do is say the phrase "I'm cold" and the seats will be heated to the optimal temperature.

Maintenance - how AI helps you take care of your car

7. Predictive Maintenance prevents malfunctions before they even appear

Cars that we are driving today are already pretty smart. They can alert you whenever something needs your attention and they can pretty precisely say what they actually need – oil, checking the engine, lights etc. The Connected Car era however equipped with the possibilities given by AI brings a whole lot more – predictive maintenance. In this case AI monitors all the sensors within the car and is set to detect any potential problems even before they occur.

AI can easily spot any changes, which may indicate failure, long before it could affect the vehicle’s performance. To go even further with this idea, thanks to the Over-The-Air Update feature, after finding a bug that can be easily fixed by a system patch, such solution can be sent to the car Over-The-Air directly by the manufacturer without the need for the customer to visit the dealership.

•     Predi    (an AI software company from California) has created an intelligent platform that uses the service order history and data from the Internet of Things to prevent breakdowns and deal with new possible ones faster.

8. Insure your car directly from the cockpit

Driving a car is not only about operating costs and repairs, but also insurance that each of us is required to purchase. In this respect, AI can be useful not only for insurance companies (  see how AI can improve the claims handling process ), but also for drivers themselves. Thanks to the appropriate software, we will remember about expiring insurance or even buy it directly from the comfort of our car, without having to visit the insurer's website or a stationary point.

•  The German company     ACTINEO,    specialising in personal injury insurance, processes and digitises 120,000. claims annually. Their ACTINEO Cockpit service is a digital manager that allows for the comprehensive management of this type of cases, control of billing costs, etc.
•  In collaboration with     Ford, Arity    provides insurers - with the driver's consent, of course - data on the driving style of the vehicle owner. In return for sharing this information, the driver is offered personalised insurance that matches his driving style. The platform’s calculations are based on "more than 440 billion miles of historical driving data from more than 23 million active telematics connections and more than eight years of data directly from cars (source: Green Car Congress).

When will AI take over the automotive industry?

In 2015, it is estimated that only 5-10% of cars had some form of AI installed. The last five years have brought the dissemination of solutions such as parking assistance, driver assistance and cruise control. However, the real boom is likely to occur within the next 8-10 years.

From now on, artificial intelligence in the automotive industry will no longer be a novelty or wealthy buyers’ whims. The spread of the Internet of Things, consumer preferences and finding ways of saving money in the manufacturing process will simply force manufacturers to do this - not only in the vehicle cockpits, but also on the production and service lines.

To this end, they will be made to cooperate with manufacturers of sensors and ultrasonic solutions (cooperation between BMW and Mobileye, Daimler from Bosch or VW and Ford with Aurora) and IT companies providing software for AI. A dependable partner who understands the potential of AI and knows how to use its power to create the  car of the future is the key to success for companies in this industry.

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