Predictive transport model and automotive. How can smart cities use data from connected vehicles?

 Accidents, traffic congestion, lack of parking lots and poor state of roads. These are the 4 Horsemen of the Road Apocalypse that on occasion haunt cities around the globe. Have they come to settle in the largest agglomerations for good? Can AI in transportation combat them and make mobility smoother, more comfortable, and safer? Practical solutions introduced by the biggest transport companies from all over the world show that it is possible. And we do not have to wait for fully self-driving cars to use the advantages of AI. The changes are happening right before our eyes.

In 1900, the number of vehicles in the USA - the only country that produced cars at the time - reached 4192 vehicles. Today, the number of motor cars is estimated to be around 600 million, and with the current growth in production, this number is expected to double in the next 30 years. Our cities are congested, polluted and in many places getting around in a car during rush hour borders on the miraculous. Not to mention the real endurance test that drivers' nerves are put to.

Government agencies and shipping companies must explore solutions that reduce the number of vehicles in cities and equip urban infrastructure and cars with tools that effectively offset the side effects of technological globalization. The  Internet of Things and artificial intelligence are coming to the rescue to facilitate a new class of intelligent transportation systems (ITS), not only for  automotive but also for rail, marine, and aircraft transportation.

By  analyzing massive amounts of data from vehicles and  connecting the road infrastructure into a seamless network of information exchange , many aspects of transportation can be successfully addressed. The benefits of using AI in this market area are not only for cities and drivers but also for transport companies, pedestrians, and the environment. The whole transport ecosystem benefits from it, not just one of its constituent parts. We should all care about the development of these technologies and the broadest possible use of them in transport.

Thanks to the above-mentioned technologies, new trends are developing, such as micro-mobility,  shared mobility or, especially in the Netherlands and Scandinavia, the idea of mobility-as-a-service (MaaS), which encourages drivers to give up their own vehicle and exchange it for one in which transport is provided as a service.

Benefits of introducing AI in transportation

According to Market Data Forecast, the global transportation AI market will be worth around $3.87 billion by 2026 and is estimated to grow at a CAGR of 15.8% between 2021 and 2026. And it's no wonder that more and more businesses are embracing these solutions. The benefits of using AI technology in transportation are truly far-reaching and, indeed, their future is looking bright. With the development of data analytics and more modern sensors gathering information, new and innovative applications are bound to emerge.

Today, key benefits of using AI in transportation include:

•  increasing transportation safety;
•  detecting market trends;
•  relieving traffic congestion;
•  reducing greenhouse gas emissions, air pollution, and noise;
•  improved transportation design and management;
•  better management of urban space and reclaiming specific urban areas for residents;
•  analyzing travel needs and pedestrian behavior.

9 use cases of AI in transportation

When talking about using AI in transportation, self-driving cars are the most often mentioned examples that stir the imagination. Although such solutions have already been tested on the city streets (e.g. Waymo and Cruise in California) and occasionally we hear news about reaching by the manufacturer the highest (5th) level of automation, we are still a little away from the dissemination of vehicles that do not need any attention of the driver.

The main challenges faced by autonomous driving remain unchanged. First,  detection of objects on the road and their categorization, and second,  making the right decisions by the neural network, decision tree, or, in most cases, complicated hybrid model.

In 95% of cases, the neural network controlling the vehicles is already behaving correctly and making the best possible decisions. But there is still a marginal 5%, and this level is the most difficult to achieve at the moment. It simply takes time and more data to "train" a neural network. With the dropping price of LIDARs [light detection and ranging sensors], high resolutions camera, and the computing power of the GPUs [graphic processing units] increasing, it is only a matter of the next few years before this barrier is overcome - first in limited controlled areas (e.g. factories and harbors), the form of autonomized truck transport, and then using city vehicles.

Meanwhile, there are already more than a dozen advanced technologies on the road today that are  taking advantage of the AI ‘’goodies’’ and changing the way we control vehicle flow, driver safety, and driving behavior. Let's take a closer look at them.

1.Traffic detection & traffic signs

If traffic regulations were boiled down to one simple rule that even a few-year-old child could understand, red and green lights would definitely be second to none. Meanwhile, there are hundreds of road accidents each year related to running the red light and not stopping the vehicle at the right moment. Many factors contribute to this, such as driver fatigue, inclement weather, misuse of cell phones while driving, or simply rushing and time pressure.

People make mistakes and always will, these cannot be avoided. However, we have started to teach the machine to recognize traffic lights and eradicate such mistakes (the first attempts were made by BMW and Mercedes). With this technology, the braking system will react automatically when the driver tries to run a red light, and thus we can prevent disaster.

2. Pedestrian detection

The unpredictability of pedestrians and their different behavior on the road is one of the main factors holding back the mass introduction of autonomous cars. Thanks to computer vision,  AI already recognizes trees, unusual objects, and pedestrians without much of a struggle, and can warn drivers of a human approaching the roadway. The problem arises when a pedestrian is carrying groceries, holding a dog on a lead, or is in a wheelchair. Their unusual shape increases the difficulty for the machine to properly identify a human. Although it must be admitted that by using various object detection functions - based on motion, textures, shapes, or gradients - it is practically 100% successful.

However, the pedestrian's intention still remains a great challenge. Will he or she step onto the road or not? Are they only walking by the side of the road, or do they intend to cross it? These elements are always ambiguous and a neural network is needed to predict them effectively. To this end, the human pose estimation method comes in handy. It is based on the dynamics of the human skeleton and is capable of predicting human intentions in real-time.

3.Traffic Flow Analysis

Noise, smog, clogged city arteries,  stressed drivers, economic losses, greenhouse gas emissions - traffic congestion and vehicle crowding in cities give rise to numerous undesirable phenomena. AI can effectively help counteract all of them and make transportation much more efficient and convenient.

By relying on in-vehicle sensors, municipal CCTV cameras, and even drones to monitor vehicle flow, the algorithms can watch and keep track of the traffic both on highways and in the city. This allows them to warn drivers of potential congestion or accidents and direct the flow of vehicles in an efficient manner. It is also invariably useful for the town and urban planners involved in constructing new roads and improving the city's infrastructure. With prior traffic analysis and the vast amount of data available,  AI can identify the best planning solutions and help reduce undesirable situations right at the planning stage.

4.Inspection of dangerous turns, traffic circles and bike lanes

On a macro scale AI can help us change the entire road network, and on a micro-scale- a single intersection or traffic circle that needs repair. The analysis of the material provided by intelligent algorithms can calculate the trajectory of vehicles entering the bend, analyze the risk of potential conflicts between vehicles - pedestrians - cyclists, the speed at which vehicles enter the bend, or the waiting time at the traffic lights. The analysis of all this invaluable information can help optimize a given road section, and improve the safety and convenience of transport.

5.Computer Vision-Powered Parking Management

Entering the city center by car and finding a parking lot is often a struggle. If we connect the city's parking lots into an efficient network of sensors that monitor available spaces, the length of time vehicles are parked, and the hours when vehicles are most heavily congested, this key aspect of traffic can be greatly enhanced. With maps embedded in vehicles, AI can facilitate finding free parking spots, alert you to potential parking overcrowding, and - something actually pretty common - allow you to find your car when you forget where you parked it.

Such solutions are particularly useful in places such as airports, sports stadiums or arenas, where traffic must be smooth, and a high volume of visitors may pose a threat to safety.

6. Automated license plate recognition

A useful application of AI and computer vision is car license plate recognition. This type of technology is often used when entering highways, tunnels, ferries, or restricted areas constrained by gates or barriers. AI helps verify whether a given vehicle is on the list of registrations that, due to the fee paid or the drivers' status, are allowed to access a given area.

License plate recognition by algorithms is also a well-proven tool in the hands of the police and security services, who in this way are able to pinpoint the route of a particular vehicle or verify the driver's alibi.

7. Road condition monitoring

Each year potholes cause $3,000,000,000 worth of damage to vehicles in the U.S. alone. Intelligent algorithms can warn drivers of surprises lurking on the roads and monitor the condition of the road surface, so they can notify the authorities in advance of potential spots that will soon need fixing. This is enabled by linking the camera to ADAS, which applies machine learning to gather real-time information from the road surface where it is moving.

In this way, the driver can be warned not only of roadway damage but also of wet surfaces, ice, potholes or dangerous road debris. All of this improves safety for travelers, prevents accidents, and saves money - both in terms of drivers' finances and city funds.

8. Automatic Traffic Incident Detection

Video surveillance has been with us on the roads for ages, but it wasn't until the system was supported by AI solutions that it became possible to detect traffic incidents more efficiently, respond faster and provide information to traffic users practically in real-time.

By linking cameras within an ITS system, using computer vision technology, and equipping vehicles with intelligent sensors, we can detect different types of accidents. Intelligent algorithms save lives, prevent serious accidents and warn road users of hazardous situations by recommending safer travel options.

 The most commonly detected traffic incidents include:

•  pedestrians or animals entering on the road;
•  vehicles moving too fast or too slowly;
•  vehicles blocking the passage;
•  detection of debris on the road;
•  identification of vehicles moving in the wrong direction

9. Driver Monitoring

Finally, there is a full category of artificial intelligence solutions that apply directly in the interior of the car and affect the drivers themselves (we covered this in more depth  in this article ). Among them, three are particularly noteworthy:

•     driver’s fatigue monitoring    - by detecting the driver's face and estimating the position of the head, the system can detect drowsiness and emotions of the driver and thus prevent an accident.
•     alerts when the driver gets distracted    - for instance, when they reach for their cell phone, veer out of their lane, or turn around in the back seat to talk to fellow passengers.
•     emergency assist systems    - when the driver is not responsive and does not operate the vehicle, the car first tries to wake the driver by braking and pulling safety belts, and if it fails pulls over and calls emergency.

AI in transportation: setting the course for change

Given the speed at which computer processing power is changing and the number of sensors from which  data is being collected , fully automated cars on city roads are likely to be a question of the nearest 5-10 years. Change is happening at an exponential rate and today's applications of AI in transportation are just the first glimpse of the possibilities offered by intelligent algorithms. Change is essential and inevitable, e.g. due to the challenges facing the global community when it comes to global warming.

An increasing number of people live in cities, own not one but two vehicles, and want to travel to work or do their shopping in comfort. Transport companies and city managers must join forces with IT companies to fully tap into the potential of AI and change transport to be more efficient, environmentally friendly and suited to the way we want to use our cities. This is the only way we can make transportation sustainable and remove obstacles on the way to a zero-carbon economy and smart cities. Otherwise, we may face a vision of the future in which scientists predict traffic congestion 10 times worse than we experience today.

 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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