Artificial Intelligence and its use cases in Automotive Industry
Artificial Intelligence has been a blessing for multiple industries, hence the automotive industry is no exception. To accelerate automotive operations, AI is the key to unlock a sustainable future and enhancing productive customer experience.
Driverless cars around the nook and corner of the city might seem like a fool’s paradise in the past, but the advancement in technology and widespread use of AI all across the world have made it possible somehow. For instance, the biggest automotive leaders including Tesla, Volvo, BMW, Volkswagen, and others are including AI to change the automotive workforce and make driving more convenient for users. Do you know, why?
In the hyper-digital era, customers’ convenience and experience are the top priority to scale, And if your organization lags behind in the same, it’s high time to reimagine the automotive industry.
And advancements in AI have made a huge contribution to the growth of the automotive industry.
As per Statista report, ” The global automotive intelligence market expected to reach at the size of 74.5 Billion US dollars in 2030”
Despite the revolution, artificial intelligence is bringing across the automotive industry, the automotive workforce needs to be tech-savvy within 5–50 years which is quite a challenge for the automotive industry in terms of upskilling the workforce.
To add more to the figure, Elon Musk 2017 stated that “There will be an autonomous vehicle in the next 10 years without any steering wheel”. If believe the prediction, we are pretty much closer to the future of autonomous vehicles in the time frame of 5 more years.
Let’s get rolled and understand where we can use AI in the automotive business operations and processes for a better driving experience and improved quality control.
Top 7 Use Cases of AI and Machine Learning in the Automotive Industry
Manufacturing is the core part of an automotive industry supply chain. A single error in the system can make or break the production process. But, using AI and Machine Learning algorithms can help automotive manufacturers to make the car-making process better and more efficient. For example, manual labour would pick parts from conveyer belts to complete the car-making process which leads to higher turnaround time and inefficiency. With AI-based systems and machine learning algorithms, robots can autonomously determine which part to pick, how to pick, and in which sequence, that’s makes the manufacturing process faster and better. Also, it reduces the amount of workforce required to complete the car-making process, And if there is any unexpected machine failure happen, robots can keep the humans in the loop for assistance and save any mishappening.
To make the car driving experience user-friendly, manufacturers have to handle a lot of work right from brainstorming the car model to designing it exactly in the same way. With AI in space and computer modeling, automotive architects can perform real-time tracking, and programmable shading to transform the traditional design process. With faster real-time tracing and better design workflow, AI reduces the time spent on design approval and sanction. Also, Machine learning and AI image datasets help architects to generate hundreds of potential designs for better workflow and product ideas for autonomous vehicles.
- Quality Control
Manually done the vehicle inspection leads to higher turnaround time, slow error-prone, and sluggish process. And maintaining quality control is the topmost priority for keeping a higher customer count. AI-based data annotation and object detection help manufacturers keep an eye to detect the defect in the vehicles. By generating data collected from the AI sensors, the AI system can tell users and architects both which part requires maintenance and which part needs to be changed with immediate effect. In addition, AI-powered quality control systems also detect the possible flaws in parts before they get installed in vehicles. Isn’t it amazing like the future in hand?
- Supply Chain
When it comes to the supply chain, the automotive industry is one of the most complex to handle with. Want to know why? On average, a single vehicle has almost 30000 distinct parts that arrive from different parts of the world across the globe. That makes the whole supply chain disruptive and the car-making process a struggle. Moreover, it’s vital for automotive manufacturers to monitor every stage of a component’s journey, and know exactly when to expect its arrival at the destination point. In this scenario, using an AI and Machine Learning powered supply chain can help manufacturers to create a fully automated system to make supply chain management decisions, adjusting routes and volumes to the predicted demand spikes for parts.
- Driver Experience
To stay competitive on the edge, automotive manufacturers are highly focused on the driving experience of a user. And to make it more effective, AI and training data can be a great help. Using an AI-powered Advanced Driver Assistance System (ADAS) gives a major boost to the driving experience by helping with car locks, auto door locks, hands-free calls, and others. The benefits are not just stopped her. Using the ADAS system also helps drivers to gain insights on traffic and weather changes, the best routes on maps, and so much more. And all these benefits are highly possible by using object detection, training data models, and natural language processing techniques. With the ADAS system, an exciting time is ahead for autonomous vehicles.
- Automotive Insurance
AI has a huge potential in the automotive industry for both insurers and drivers side together in case of an accident. The application of AI algorithms can speed up the process of insurance claims during an accident and mishap. On the driver’s side, AI capabilities like image datasets, and object detection can help drivers to gather incident data and fill out claims efficiently. On the other hand, insurers can use image processing and AI capability to do the vehicle damage analysis better to get rid of discrepancies and process the claims faster.
- Passenger Experience
In the digital era, customers today expect convenience in whatever they put their hand to. Considering passenger safety on road, automotive manufacturers are enhancing their vehicles with all kinds of AI-powered capabilities like image data, object identification, and NLP to upgrade their experience. With the image recognition model, drivers can easily scan the state of the driver and get their full information with a click. Also, by using speck commands passengers can easily listen to music, watch movies, order food, and others while being on the road.
Benefits of AI in the Automotive Industry
Implementation of Artificial Intelligence in the automotive industry benefits not only the vehicle manufacturers but gives a boost to vehicle part suppliers, vehicle rental enterprises, and all the businesses that are related to the automotive and supply chain domain. Some of Ai’s benefits are listed below, have a look-
- Predictive maintenance is accelerated
Using AI-based systems automotive industry can use the full potential of AI and collect data from multiple resources, and sensors to cover workforce management, inventory management, operational planning, and safe driving assistance to users. By leveraging Machine Learning technologies, automotive industries can also predict malfunctions in autonomous vehicles and take corrective action before any casualty and mishap. All in all, this helps the automotive industry to work at prime performance levels, and save time & money both together.
- Vehicle maintenance recommendations get improved
On average, a person takes the vehicle maintenance in an emergency or when an issue erupts. But, what if they get an early sign of a predictive issue. The machine learning algorithms collect data from the AI sensors in-built into the vehicle and keep track of a vehicle’s part health and raise a concern when it requires maintenance. This way driver can take precautionary measures by getting the vehicle maintained and inspected to avoid a breakdown.
- Driver Behavior Analytics is accelerated
AI and deep learning models based on automotive applications offer a plethora of valuable insights and analytics to detect driver’s behavior accurately. Using these AI sensors and systems can easily detect drive behavior and provide warning signals to avoid accidents. In addition, if the driver is distracted by any circumstances, AI signals can also alert drivers and give them early signs for protection by leveraging real-time driver distraction detection techniques.
High Time to give AI a front seat in Automotive Industry
The automotive industry is seeing increased competition, cost pressure, and volatility, Evan a small disruption can make or break an enterprise’s image. The inclusion of AI and machine learning capabilities can be a game-changer for the automotive industry. It is possible for automotive manufacturers to deploy AI technologies for designing and building new prototypes, improving supply chain efficiency, and enabling efficient maintenance of both factory equipment and vehicles on the road. And the high time to adopt these AI technologies is now. If you have an opportunity to leverage the same, act on it before it’s too late.
Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.
Published in Telematics Wire