5 Ways AI in the automotive industry is making an impact

Wondering about 5 ways AI in the automotive industry is making an impact? 

This is an excellent market with many opportunities to be explored.

According to a study, “the global AI market will reach $284.6 in the year 2025.”

Advancements in artificial intelligence continue to develop and further push industries into what can often feel like the stuff of science fiction films. Whether it be autonomous cars, taxis, and lorries or exponential growth in the IoT space, it can be hard to keep up to date with current trends or take stock of how AI is impacting the industry.

In this blog post, aimed at those who are in the automotive industry, and interested in artificial intelligence and technology, we explore five use cases of AI in the automotive industry.

Five Use Cases of AI in the Automotive Industry

AI in the automotive industry is making an impact in the following ways:

Driverless Automobiles

What once was the thing of science fiction in movies, such as the first release of Total Recall with its driverless taxis, artificial intelligence is advancing at such a pace that tech firms have been developing technology to facilitate the introduction of autonomous automobiles and lorries.

The concept is no longer the stuff of blockbuster movies, but soon a very common use case of artificial intelligence in the automotive industry!

Historically, car manufacturers and tech companies that produce driverless technology had to adhere to strict guidelines, not to mention, a human had to always be present and ready to take over in the event of an emergency

Earlier this year, California granted approval and new legislation to allow driverless cars *without* a human operator!

The new rules and legislation took over 3 years to be defined and were often seen as a major blocker by firms such as Alphabet, Uber, GM, and Ford.

States such as Arizona, Nevada, and Michigan are also starting to allow autonomous cars with no human operator, and with acceptance being gradually eased into the public domain.

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We‘re on the cusp of a driverless car revolution that only stands to pick up the pace as the technology iterates and keeps improving.

Audi

Historically, one of the main challenges of driverless car technology that was identified by Google back in 2012 was the “hand-off” problem. 

In essence, how can the technology alert and engage a human when the machine or artificial intelligence has identified a risk?  With over 35,000 road traffic deaths per year in the US alone, it‘s not something to be treated lightly. 

That said, it‘s not an impossible challenge to solve and it‘s just what German car manufacturer Audi set out to do.

Audi spent years with their engineers and psychologists and effectively “taught” a car how to drive safely, their main emphasis, in conjunction with basic road maneuvers was on the human-to-machine interface that was responsible for the “hand-off”.

The AI solution was integrated with the luxury A8 model and uses facial recognition cameras to monitor the driver‘s expression and can also detect if the steering wheel is being touched. 

If the car identifies that that human isn‘t paying attention, it will “annoy” them with visual or audio cues.  If those don‘t work, the car will tighten the seatbelt and pump the breaks!

Finally, if the human still hasn‘t acknowledged there is an issue, the car will turn on the hazard lights and slot to a stop, and unlock the doors.

Whilst this hasn‘t completely entered the mainstream, it does give other companies a good blueprint to work from in terms of how humans can be alerted to take control of a driverless car.  Make no doubt about it, this technology will soon enter the mainstream and be in a garage near you!

Internet of Things and Connectivity (IoT)

Millions of intelligent devices get connected to the internet every day from Fit Bits to monitor health to smartphones. It’s estimated that more than 250 million vehicles will be connected to the internet in some capacity but what does this mean for the automotive industry?

As automobiles rely ever more on technology, electrical components, and software; manufacturers will have to collaborate with software development teams and other firms in the tech sector. 

All of this will help facilitate the successful integration of cars that depend on internet connectivity and IoT devices.

With such connectivity, cars will become increasingly vulnerable to malicious hackers, it‘s with this in mind that a firm called Cyber 2 Automotive Security (or C2a) made their patented Stamper technology available to car manufacturers worldwide through a royalty-free license.

C2a‘s Stamper technology is part of a suite of solutions that are designed to protect cars against online and connected attacks. 

Back in 2016, hackers took control of a Tesla from over 10 miles away, and with new cyber-attacks being dreamt up each day, it’s just a matter of time before ransomware-type security hackles are implemented.

Stamper implements a security layer for connected cars and devices and actively protects every semiconductor and processor in the car. 

You can think of it as a firewall of sorts that grants or denies access to specific car functions whilst identifying anomalies and performing real-time diagnostics to ensure that everything is working as it should.

The firm is giving away the technology but will make money from other components in their product set and as the number of connected cars only sets to increase, one thing is for certain, firms like C2a will be perfectly positioned to take a slice of this market.

In the future, we predict that the integration of artificial intelligence and IoT devices in the future will allow manufacturers to update autonomous vehicles with additional features such as:

• Predictive maintenance, read more on it here;
• Repair scheduling;
• Identification of performance issues;
• Using smart sensors to identify medical emergencies;
• Automatic toll collection.

Risk Identification and Emotion Detection

Detection of emotion in streams of text or documents has been available for a number of years, many providers such as IBM, Microsoft, and Social Opinion offer sentiment analysis APIs that can do this.

Identifying the mood in a document is one thing, but detecting the mood or emotion of a human in real-time is a completely different matter altogether. 

That said, it‘s now possible for machines to identify the mood a human is expressing by uploading pictures and processing them through machine learning algorithms and calculating probabilities in terms of key emotions such as anger, joy, sadness, and so on.

Affectiva

A firm called Affectiva, which is an MIT Media Lab spinoff, has taken this a step further, and has recently launched a new service dubbed “Automotive AI”.

The service allows manufacturers of driverless vehicles to effectively track a human driver‘s emotional response and can detect emotions such as joy, surprise, fear, or anger.

The innovative solution uses regular RGB cameras and (near) infrared cameras to arrive at confidence scores using signals such as eyeblink per second and can identify things like yawning, drowsiness, and other signs of driver fatigue.

Being able to identify signals like this in human drivers, coupled with AI-assisted cars will help reduce the risk of road traffic accidents due to fatigue. The firm has even reported that Automotive AI could be used to identify if a driver was under the influence of drugs or alcohol.

BMW, Nauto, and the Toyota Research Institute (TRI)

The German car manufacturer BMW partnered with the Allianz insurance group to establish AI-powered products to help improve driver safety and fleet management. 

Using deep learning technology, and leveraging Nauto‘s cloud-based AI platform, a solution was developed to track driver alertness, near misses, and unsafe driving habits!  Make no doubt about it, auditing this data will have an effect on insurance premiums in the future!

Machine Learning and Assisted Driving

Machine learning algorithms are increasingly being applied to a wide number of use cases, from analyzing and processing vast quantities of Big Data to helping healthcare professionals identify patterns in sets of patient test results, thereby helping improve medical diagnosis.

Machine learning, which is a branch of artificial intelligence, effectively attempts to replicate the way in which humans learn (i.e. by the repetition of tasks using historical data). How much is machine learning impacting the automotive industry?

Toyota, who is one of the biggest car manufacturers on the planet, and their new company TRI – Toyota Research Institute (which we just mentioned earlier) are planning on using machine learning to help tackle Japan’s aging population where the number of people aged 60+ is expected to jump from 25 percent to 40 percent in the next 30 years

This shift in demographics isn‘t just isolated to Japan, in Northern America, over 65s will account for approximately 20 percent of the US population,

To help keep an aging population mobile and ensure they can still use their cars, Toyota is undertaking considerable R&D activities in the form of Human Support Robots that help people maintain a level of mobility they might otherwise not have.

Such technology, whilst still in the R&D phase might be able to help an older population get in and out of cars.  You can read more about the technology here.

Advanced Assisted Driving

Mobileye, an Israeli company that supplies car manufacturers with computer vision technology and uses a single camera, coupled with machine learning, has developed a solution that augments drivers‘ capabilities by being able to identify the speed limit from road signs or by identifying pedestrians on roads thereby being able to automatically trigger an automatic braking system.

The technology caught the eye of Intel who proposed a $15.3 billion acquisition! 

It‘s not hard to understand why the chipmaker has decided to carve itself a space in the market given that NVIDIA has been forming partnerships with car manufacturers and helping shape the autonomous car industry.

Robotics and Defect Detection

Whilst there is no doubt that artificial intelligence is pushing a driverless car revolution and will improve road safety in the long term, the benefits of artificial intelligence in car manufacturing plants are also worth mentioning.

Robots have been used in automotive manufacturing plants for many years, and the machinery that gets used in the manufacturing production line is no doubt impressive.

In other words, robots are an example of artificial intelligence in the automotive industry that has been in use for quite some time and will be even more advanced.

A report published by McKinsey stated that artificial intelligence will reduce car manufacturing downtime by leveraging sensors and complex algorithms to monitor manufacturing equipment round the clock.

The report went on to say that:

AI-based algorithms can digest masses of data from vibration sensors and other sources, detect anomalies, separate errors from background noise, diagnose the problem, and predict if a breakdown is likely or imminent.

…and

More than 20 percent increase in equipment availability, up to 25 percent lower inspection costs, and up to 10 percent lower total annual maintenance costs.

The report also said that cars that roll off the production line will be of better quality as artificial intelligence quality control can detect production anomalies more accurately than humans ever can.

The stated artificial intelligence algorithms can be used to predict and forecast orders, thereby, reducing excess stock by up to 50%!

With findings like these, it‘s easy to see how artificial intelligence-powered robotics and algorithms will help drive business efficiencies in terms of cost across multiple steps of the manufacturing and distribution process.

If you‘re interested, you can read the report in full here.

Summing up on AI in Automotive Industry

In this blog post, we‘ve looked at five use cases of artificial intelligence in the automotive industry and its positive impacts.

We‘ve seen how the autonomous car revolution is upon us and how automobiles of the future will be deeply integrated with the internet.

We‘ve also seen how emotion detection can be used to improve driver safety and help insurance companies provide tailored insurance quotes, and finally, we‘ve discussed how artificial intelligence-powered robots, machine learning algorithms, and data science will help businesses predict and forecast stock orders, help drive business savings and improve the quality of automobiles that work their way through the production line.

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Write to us your initial project specifications, and one of our managers will get back to you for further guidance on how we can help you build great software products. 

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