Why AI in Automotive?

AI is a technology that can rapidly collect, process, analyze, and act on large disparate datasets, perform complex functions, and repeatedly execute workflows and processes with precision. When applied in the automotive industry, these capabilities can be used to improve human, vehicle, and environmental safety, operations, and performance. For example, AI can generate insights about vehicles, their drivers, and road conditions or support informed, near-real-time decision-making, both human and automated.

Benefits of AI in Automotive

AI is already delivering significant benefits across the automotive industry and offers the potential for even greater impact as AI advances and is applied to more automotive use cases.

Driver Benefits

In today’s connected economy, drivers expect their vehicles to support safe, efficient, and personalized driving experiences. Some AI-enabled driver benefits include:

• Improved safety: AI-powered systems, such as advanced driver-assistance systems (ADAS) and autonomous driving technologies, help reduce accidents by detecting, reacting, and alerting on hazards faster than human drivers.
• Personalized in-vehicle experiences: AI can automatically adjust vehicle features, including seat, wheel height, audio, temperature, and display settings, according to user preferences, enhancing the overall driving experience.
• Personalized assistance: Natural language processing (NLP) voice recognition can respond to or create intuitive, conversational exchanges with the driver based on their language, interests, and directions for hands-free control.
• Improved navigation: AI-enhanced navigation systems can provide real-time traffic updates and alternative route suggestions.
• Smoother insurance processes: In the event of an accident, AI-enabled object detection and collected vehicle data, including images or videos, can help drivers collect incident information needed to complete claims. Insurers can also benefit from AI-enhanced systems that can efficiently process images and analyze vehicle damage to minimize discrepancies and more quickly process claims.

Fleet Benefits

All fleets, such as those for public transit, heavy machinery equipment, and cargo, can leverage AI to improve efficiency, performance, and safety. Some benefits include:

• Improved efficiency and cost savings: Made possible by collecting vehicle and environmental data and fleet requirements, AI can be used to optimize route planning to reduce fuel consumption and transportation timelines and to help reduce emissions.
• Improved visibility: AI-enabled sensors can collect, store, and analyze vehicle and roadway condition data from GPS technology and use computer vision to monitor vehicle and passenger conditions.
• Risk management: Computer vision-based AI, applied inside and outside the vehicle, can help monitor for theft prevention, driver danger situations, and cargo hold security. This helps to improve driver safety, decrease shrinkage due to cargo losses, and reduce costs associated with vehicle downtime and repairs due to damage. 
• Predictive maintenance: AI can analyze vehicle data to predict potential failures and proactively alert on service needs, helping to reduce repair costs, minimize downtime, and extend the vehicle life-span.
• Optimized logistics: AI-based work zone analytics can help fleet managers adjust timelines for current supply chain logistics. AI-based systems can monitor heavy machine equipment usage and propose adjustments for optimal performance in varying conditions. AI can be used to autonomously or semi-autonomously control machinery in hazardous environments.

Manufacturing Benefits

AI is enabling automotive manufacturers to improve quality, operational efficiency, supply chain logistics, and innovation in vehicle design and production workflows. Some of the benefits for automotive manufacturers include:

• Streamlined manufacturing processes: AI can be used to streamline production lines through automation, quality control, and supply chain optimization, resulting in cost savings and higher product quality.
• Safer, more efficient production: AI can automate processes to improve consistency, repeatability, and throughput and minimize human intervention, especially for repetitive tasks. It can also monitor production quality and machine conditions and send alerts on issues. AI-augmented smart robots can be used to execute one or more targeted physical tasks to assist human workers or for tasks in potentially hazardous areas to protect worker safety.
• Increased supply chain visibility: AI can be used to analyze, visualize, and explore connected data and the relationships between products (vehicles), people (drivers), and manufacturing operations. This can help manage the supply chain and adjust volumes and routes as needed to meet demand or optimize delivery.
• Accelerated vehicle design and development: From ideation to production, AI-aided design and physics-informed AI (PIAI) can be used to explore, evaluate, and prototype more-efficient and safer designs, optimized through simulated conditions and based on physical realities. Digital twins, virtual representations of real-world products informed by real-world data, can also be used to help design and test improvements, predict variable outcomes, and optimize performance without affecting the actual vehicle.
• Data-driven insights: AI analysis of vast amounts of data from connected vehicles can provide insights for improving products and services. AI analysis of sales and production data can be used to regulate production, inform adjustments to features and designs based on customer preferences, and help optimize sales processes.

AI in Automotive Use Cases

AI-enabled sensors, AI-based computer vision solutions, machine and deep learning, natural language processing, and generative AI (GenAI) are all being used to enable AI use cases in the automotive industry. As a result, day-to-day driving, vehicle performance, and manufacturing processes are becoming safer, smarter, more efficient, and more cost-effective. Here are a few additional ways these AI technologies are being applied:

• For autonomous and assisted driving, AI enables self-driving cars by processing data from sensors, cameras, and radar to navigate, monitor road conditions, and react and alert to hazards and unexpected situations in near-real time. Features, including lane assist, also help to improve driver safety. 
• Future connected cars will enable data sharing with devices and remote services to communicate with other vehicles to maintain safe driving distances, help traffic managers understand road conditions to account for traffic flow in route planning and accident prevention, and provide drivers with on-demand insights, onboard diagnostics, proactive repair alerts, and streaming infotainment. A novel use case includes communication with drive-through payment systems to pay for a purchase.
• In automotive manufacturing, AI can enable dark factories that operate through automated equipment with minimal human activity—even in the dark without lights. Also called lights-out manufacturing, AI can increase efficiency and production capacity with error-free quality.

Future of AI in Automotive

The rapid expansion in technological capabilities is driving innovative use cases and, with it, consumer and industry expectations. The automotive industry is also undergoing a technological transformation from analog to fully digital architectures while at the same time moving from combustion engines to electric vehicles (EV) and from fixed-function devices to software-defined computing. This digitization is essential for supporting next-gen experiences and efficiencies.

AI use cases in automotive will continue to harness data to add more value to drivers, vehicles, and their manufacturing processes, resulting in future-forward advancements for individuals and the industry.

That being said, capitalizing on AI opportunities in automotive is not possible without an investment in time and resources as well as considerations for human safety, data privacy and security, compliance and regulatory requirements, and a responsible approach to AI. Working with a technology partner to plan AI initiatives can help ensure these considerations are accounted for and that investment road maps align business needs and existing infrastructure with right-fit AI-based solutions for greater return.