The AI/ML technologies driving the automotive digital revolution

automotive manufacturing robots welding

September 28, 2023By Peter Maithel, Infor Automotive Industry Principal

Artificial intelligence (AI) and machine learning (ML), or AI/ML, have enabled the automotive industry to enhance multiple areas of design, operational efficiency, and driver experience.

Auto manufacturers have used AI/ML to make vehicles safer and the driving experience more automated to reduce human-caused accidents. Manufacturing processes become more precise and efficient under the guidance of AI/ML systems, resulting in lower costs and waste. Advanced AI/ML algorithms help fleet technicians predict maintenance to reduce unexpected breakdowns and extend the life of vehicles.

These are just a few ways AI/ML have impacted the automotive industry.

In this article, we’ll provide a more in-depth look at how companies can deploy AI/ML to create a safer, more efficient, and more sustainable future in transportation.

1. Autonomous driving technologies 

Autonomous driving technologies employ AI/ML algorithms to enable self-driving and driver assistance capabilities in vehicles. Autonomous vehicles (AVs) are still in development, with only a few companies allowed to test on the open roads.

Although many see AVs as the future, we’re still far from widespread adoption.

Many automotive manufacturers have shifted their focus from full (level 5) autonomy to advanced driver-assistance systems (ADAS) to capitalize on immediate business opportunities.

Examples of the AI/ML systems used in autonomous driving technologies include: 

  • Computer vision gives eyes to AI/ML systems
    This field of AI uses advanced convolutional neural networks (CNNs) to interpret the visual world, recognize objects, and navigate through environments safely.

  • Sensor fusion paints the complete picture
    Sensor fusion is procuring data from diverse sensors such as LiDAR, radar, cameras, and ultrasonic sensors to construct an accurate picture of the vehicle’s surroundings.

  • Route optimization enhances efficiency and safety
    Route optimization relies on AI/ML to analyze traffic conditions, road geometry, and pedestrian presence. This data enables the AI/ML system to determine the quickest, most fuel-efficient, and safest route possible. 

2. Predictive maintenance systems 

Predictive maintenance uses AI/ML and the Internet of Things (IoT) to foresee and preemptively address vehicle component failures before they occur. 

This enhances reliability and operational efficiency across multiple areas of operation:

  • On-road performance
  • Fleet management
  • After-sales service
Midwest Wheel saves customers time and money by using AI to recommend parts
FEATURED CASE STUDY: Midwest Wheel, a prominent truck parts distributor, utilizes predictive analytics powered by Infor® Coleman AI to revolutionize proactive vehicle maintenance for their customers. Through AI-enhanced insights, they've accelerated sales order processing by 30%. Customers now receive parts for preemptive vehicle upkeep in a timely fashion and cost-effective manner. 


Click here to view the case study: Midwest Wheel


Let's examine in greater detail the multiple benefits and uses of predictive maintenance.

Prevent breakdowns with component monitoring

Component monitoring systems identify wear patterns and anomalies with the help of AI/ML. Identifying a faulty component early helps reduce unexpected equipment failures and limit vehicle downtime.

Create a safer driving environment with AI/ML

Failure to address potential vehicle malfunctions could lead to catastrophic accidents and jeopardize the safety of fleet drivers and the general public. This is crucial in autonomous vehicles (AVs), where system reliability is vital for safe operation. AI/ML help prevent these potential failures before they happen to ensure the vehicle operates safely and optimally.

AI/ML optimize operations costs 

Operational cost optimization employs AI and ML to automate routine tasks, enhance human decision-making, and mitigate risks. Offloading these responsibilities then helps you save on operations costs through reduced labor, increased productivity, minimized waste, and optimal resource allocation.

3. Automotive manufacturing optimization

Automotive manufacturers and suppliers face substantial challenges in resource allocation, quality control, and operational improvement. AI/ML provide sophisticated tools for helping you overcome these challenges.

For example, Infor® PLM Discrete is a comprehensive product lifecycle management solution developed for manufacturers that integrates with your enterprise resource planning (ERP) and computer-aided design (CAD) systems to streamline product development processes and speed time to market. Our system can enhance design proficiency, accelerate product development, and minimize costs for new product launches. 

Here’s a closer look at the applications of AI/ML technologies in automotive manufacturing.

Improve defect detection with automated quality inspection
Leveraging AI for quality inspection reduces human error and improves the detection of defects in manufactured parts. This ensures each component your company produces is of the highest quality, ensuring a safe and reliable final product.

Optimize your supply chains with ML
Applying machine learning algorithms to supply chain operations allows auto manufacturers and suppliers to predict demand, manage inventory, and optimize logistics. These predictive ML algorithms can help you reduce costs, improve product availability, and refine production scheduling.

Reduce carbon emissions with AI-driven energy management
Automotive manufacturers can use AI to optimize energy usage with automated control systems. Limiting your energy consumption to only what you need to use reduces operational costs, minimizes energy wastage, and reduces carbon emissions.

4. Simulation and design

Simulation and design phases often encounter challenges in creating accurate models for predicting real-world performance and feasibility. 

AI/ML synthesize design elements, materials, and engineering principles to help auto manufacturers develop enhanced vehicles. The technologies also can create more dependable models, enable quick design modifications for optimal performance, and identify possible issues before building physical prototypes.

With this valuable insight, auto manufacturers can reduce development times and improve the success rate of new vehicle design projects.

Wide-ranging applications of AI/ML for simulation and design in the automotive industry include: 

  • Test-drive concept vehicles with advanced simulation tools
    AI/ML provide advanced simulation tools to test vehicle designs in various conditions and scenarios—all without physical fabrication. These simulations help you find and fix potential problems early, saving time and resources for new product development.

  • Enhance vehicle appearance and functionality with AI/ML
    AI/ML assist in formulating vehicle designs that balance aesthetics, functionality, and sustainability. Integrating these essential attributes contributes to the overall advancement and innovation in vehicle design, propelling the automotive industry into the future.

  • Structural integrity analysis finds weaknesses in designs
    Using ML systems to analyze the structural integrity of products or vehicles helps identify weaknesses and discover improvements. Integrating safety standards into ML algorithms will help you create components or vehicles that are safer, longer lasting, and in compliance with regulations.

5. Cybersecurity

As we become more reliant on connected and autonomous driving technologies, securing the backend against cyber threats has become a concern in the automotive industry.

Cyberattacks can range from unauthorized access to critical systems, data breaches exposing sensitive user information, or more sophisticated attacks aimed at compromising vehicle functionality and safety. 

AI/ML work around the clock to detect, counteract, and prevent these malicious attacks. A robust cybersecurity network reinforces consumer trust and makes adoption easier throughout populations.

Benefits and applications AI/ML provide to cybersecurity in the automotive industry include: 

Detection of anomalies for fast remediation
AI/ML actively monitor vehicle systems to identify abnormal patterns or deviations in real time. Immediate detection of any unauthorized or malicious attacks enables prompt remedial action and minimizes the risk of system data breaches.

Threat intelligence and predictive analytics enhance cybersecurity
Gain insights into emerging threats and vulnerabilities by analyzing vast datasets and leveraging threat intelligence. AI/ML enhance security against cyberattacks and data breaches by responding to threats before they cause considerable damage.

Automated incident response helps contain cyberattacks
Streamline incident response through automation without manual intervention. AI/ML's swift response to cyberattacks automatically contains threats, minimizes potential damage, and reduces the burden on human resources.

6. Customer interaction and sales

AI/ML technologies are reshaping customer interactions and sales strategies for the automotive industry by analyzing consumer behavior and us predictive analytics to create more effective engagements and increase conversion rates. Equipped with this data, many companies have shifted to deliver a personalized and streamlined consumer experience.

How can you use AI/ML in your customer engagement strategy? Check out the applications below.

Personalize your marketing to connect with customers
AI/ML analyze data to create more personalized marketing messages for potential customers. More effective and targeted advertising can increase the likelihood of customer conversion and brand loyalty.

Chatbots and virtual assistants simplify operations
These tools use AI to interact with customers in real time, answering questions and providing information on automotive products and services. The immediate and accurate response from these AI tools can enhance your customer service, improve brand perception, and increase customer retention.

Forecast sales with predictive analytics powered by AI/ML
AI/ML leverage vast datasets to predict future sales trends and customer preferences. These valuable insights allow automotive suppliers and manufacturers to optimize inventory and pricing strategies while maximizing profit margins.

Accelerate your digital transformation with Infor

It’s clear AI/ML will be the driving force behind the automotive industry’s digital revolution. Embracing these advanced technologies empowers your company to transform its manufacturing, design, cybersecurity, and customer interaction processes.

Infor’s automotive software and AI/ML solutions have helped many automotive companies boost profitability by optimizing operations and enhancing innovation. Contact us today and let’s drive into a more connected, intelligent, and sustainable automotive future.

In April 2024, Infor Coleman AI was renamed and simplified to Infor Artificial Intelligence (AI). The functionality and services of Infor Coleman AI are still available within the InforOS ecosystem, even as the Coleman name has been retired.

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