AI in the automotive industry: use cases, success stories & adoption guidelines
October 10, 2024
AI in automotive is used to facilitate vehicle design, optimize manufacturing processes, ensure a superior driving experience, and help automakers, dealerships, and insurers drive sales and improve customer service.
With solid experience in AI consulting, Itransition develops advanced solutions to help companies overcome the unique challenges of this industry.
AI in the automotive industry: market statistics
the estimated size of the global automotive AI market in 2024
Precedence Research, 2024
of automotive enterprises surveyed have actively deployed or are exploring AI
IBM, 2023
of automotive enterprises surveyed have accelerated AI investments in the past 2 years
IBM, 2023
In which of the following ways, if any, is your organization using AI and automation today? Please select all that apply.
[Among IT Professionals at companies currently exploring or deploying AI]
Scheme title: Use cases of AI and automation in the automotive sector
Data source: IBM Global AI Adoption Index Report
AI use cases in transportation
As an emerging tech trend in the transportation sector, AI promises to make driving less stressful and more secure and, on a larger scale, facilitates logistics operations involving large fleets of vehicles.
Personal voice assistants
Whether it’s a third-party personal assistant like Alexa and Siri or a proprietary solution, most automotive companies currently implement AI systems that use voice recognition to interact with drivers. These virtual assistants can adjust the interior temperature, provide information on fuel level and consumption, make calls, and change radio stations. Additionally, such tools offer high levels of personalization by remembering drivers' preferences and suggesting adjustments based on the context.
Fleet management
AI systems rely on connected vehicle technology and algorithms to process data on road conditions, traffic in a specific area, weather, and other environmental information and help fleet managers perform various tasks. These include identifying the most efficient routes, predicting potential delays, coordinating drivers, and rescheduling deliveries accordingly. Furthermore, logistics companies can use these solutions to optimize order distribution across their freight vehicles based on cargo weight, volume, and delivery points.
Advanced driver-assistance systems
ADAS solutions integrate AI-powered cameras and sensors to provide semi-autonomous driving functionality like adaptive cruise control, traffic sign recognition, forward-collision warning, and drowsiness detection. Previously found in premium cars, these systems are turning into a staple due to their ability to ensure better vehicle control even in challenging driving conditions, which helps minimize the risk of road accidents and boost vehicle safety.
Self-driving vehicles
Fully autonomous vehicles embody the highest level of driving autonomy according to the SAE six-level classification, as they leverage AI-powered ADAS systems that can control the vehicle under any conditions. These typically use multiple types of sensors (visual, radar, lidar, INS, and ultrasonic sensors) to gather data from the surrounding environment, which is then processed with AI algorithms to enable autonomous vehicle operation. Despite complex ethical implications, public concerns, and the need for further experimentation, we can already find promising examples of this technology, such as self-driving taxis and truck platooning systems.
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AI use cases in vehicle manufacturing
AI can be applied across all stages of the value chain, starting from vehicle development and production. Modern manufacturers extensively rely on AI to design smart cars, make assembly more efficient, and streamline supply chain management.
Generative design
Forward-looking automotive manufacturing companies are already using generative design software to create more durable and sustainable automotive parts. Generative AI-based systems can create hundreds of design variations for a specific component based on the set of parameters defined by designers and engineers, who then select the most suitable option.
Vehicle design simulation
Automakers extensively use digital twins to simulate how certain design decisions impact their final products. By feeding machine learning systems with historical and real-time sensor data on speed, acceleration, fuel efficiency, and other metrics, engineers and designers can understand how their ideas translate into vehicle performance without costly physical prototype testing. For example, this includes predicting how driving style and particular weather conditions impact battery performance in electric vehicles.
Automated vehicle assembly
Nowadays, AI-powered robots are increasingly present on the assembly lines of major car manufacturers. These manufacturing solutions can easily identify components via computer vision and manipulate them with pinpoint precision through mechanical arms, which makes them valuable tools for improving production outputs while mitigating physical workloads.
Quality control
While many automakers already use rule-based anomaly detection for quality control, this approach can fail to detect minor or new types of defects since rules must be updated regularly and can hardly cover the entire range of potential anomalies. Quality control solutions powered by deep learning and computer vision can go beyond simple anomaly detection and identify multiple types of defects. This almost completely eliminates the need for human intervention and significantly increases quality control efficiency.
Manufacturing equipment predictive maintenance
Traditionally, technicians perform equipment maintenance according to a predefined schedule to ensure that industrial machinery doesn't fail unexpectedly. Instead, IoT sensors can gather data from machinery parts and send this data to an ML-based system that detects anomalies, such as performance deviations, and alerts employees about potential failures. This way, automotive manufacturers can cut maintenance costs and minimize equipment downtime.
Production scheduling
AI-powered solutions can predict product demand based on economic conditions and industry trends, allowing manufacturers to adjust output in line with these forecasts and manage inventory. Combined with other Industry 4.0 technologies like IoT, AI systems can analyze additional real-time information on shipments to maximize supply chain visibility and help managers refine production planning and distribution.
AI use cases in sales & customer service
Artificial intelligence is radically changing the way vehicle manufacturers, dealers, and insurers approach sales and service management, enabling them to provide drivers with a better customer experience while mitigating risks like vehicle breakdown or insurance fraud.
Customer engagement
AI has easily found its way into the toolkit of automakers’ marketing and sales departments. Organizations often rely on AI-powered corporate software like CRM systems to personalize advertisements based on lead and customer data, automate lead management, forecast product demand according to market trends, and optimize their marketing and sales strategies. Furthermore, sales teams can use these solutions to identify cross-selling and upselling opportunities according to customer behavior and interests.
Vehicle diagnostics
With the help of AI and predictive analytics, car manufacturers can gather data on vehicle operation and usage conditions and assess how their models perform in real-life for product development purposes. At the same time, diagnostics systems detect anomalous conditions through smart sensors and alert drivers about potential technical issues so that they can timely turn to car dealer servicing or independent workshops.
Customer service chatbots
Conversational AI is an increasingly popular tool for enhancing relationships between customers and brands and increasing brand loyalty. AI chatbots can take over employees’ mundane tasks like scheduling test drives, helping customers with car model selection, answering customers’ questions about car features, and gathering customer feedback.
Vehicle damage assessment
Some insurance companies currently leverage AI and computer vision to automate vehicle inspection and facilitate car accident case resolution. For instance, drivers can use their mobile phone cameras to take pictures of damaged cars and attach them when filing insurance claims. An AI-powered system analyzes such images and evaluates the extent of the damage through computer vision algorithms, making the assessment process much faster and more objective.
Vehicle insurance fraud detection
Every year, insurers pay out billions of dollars in fraudulent claims, whose cost is reflected in higher premiums for policyholders. ML systems for fraud detection can use natural language processing to spot inconsistencies in insurance claims and flag them for human review. With the help of predictive analytics, insurers can also estimate the risk of fraud based on the respective policyholder profiles.
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AI adoption benefits in the automotive industry
Real-life AI applications in automotive
Hyundai’s generative vehicle design
Hyundai Motor Group applies AI-driven generative design to redefine product development and revolutionize how vehicles traverse. The company partnered with product innovation studio Sundberg-Ferarhas to build the Ultimate Mobility Vehicle “Elevate”, capable of traversing the most challenging terrain. Elevate can transform from a four-wheeled vehicle into a four-legged walking robot, and its inventors claim the vehicle will prove most useful for search and rescue.
Generative design really allows us to tackle complex problems that would take somebody a lot more time than they have to go through different analyses. It’s a mind multiplier, I like to call it, where a single designer or engineer can go through perhaps dozens or hundreds of different design iterations. It allows them to see things that they may not have otherwise considered.
BMW’s design simulation for prototyping
BMW adopted Monolith, an AI-based software widely used among aerospace, automotive, and industrial engineering companies, to facilitate vehicle development. Specifically, BMW engineers relied on this solution to accurately predict a car’s aerodynamic performance without building physical prototypes. Additionally, BMW’s crash test engineering team was able to apply Monolith to predict the force on a passenger’s tibia during a crash without conducting physical tests, and much earlier in the development process.
When the intractable physics of a complex vehicle system means it can’t be truly solved via simulation, AI and self-learning models can fill the gap to instantly understand and predict vehicle performance. This offers engineers a tremendous new tool to do less testing and more learning from their data by reducing the number of required simulations and physical tests while critically making existing data more valuable.
Audi’s vehicle quality control
Audi has been using computer vision for visual inspection of sheet metal components in vehicles for several years. These AI systems can detect even the smallest cracks in sheet metal parts during production, allowing the company to significantly reduce defective parts in finished products. Recently, the company also implemented AI-based quality control of spot welds in car body constructions, starting at its Neckarsulm plant. The solution analyzes approximately 1.5 million spot welds on 300 vehicles per shift at this factory alone, replacing manual ultrasound-based monitoring that could analyze only 5,000 spot welds per vehicle.
Mercedes-Benz’s autonomous vehicle system
Mercedes partnered with leading GPU provider Nvidia to enhance its new cars with autonomous driving capabilities. The company will rely on a centralized processing architecture based on NVIDIA DRIVE Orin, capable of performing 254 trillion operations per second. The solution can handle any type of procedure, including stopping for pedestrians, navigating roundabouts, or even maneuvering around construction vehicles, enabling secure automated driving in urban environments with complex traffic patterns.
Tesla’s Trip Planner navigation feature
Probably the world’s best-known EV manufacturer and a pioneer of autonomous driving technology, Tesla is constantly expanding its offering of AI-enabled services and products to enhance driver comfort. For instance, the company has enriched its popular Tesla app with a trip planning feature that uses AI algorithms to automatically calculate the fastest route. The solution factors in driving style, outside temperature, traffic, and many other metrics to predict travel times and energy consumption. Furthermore, it takes into account the location and availability of Tesla Superchargers along the way to minimize queues at charging stations and optimize charging times.
AI in automotive adoption challenges
Despite the many benefits that AI can bring to drivers and automakers, companies should consider potential issues associated with implementing this technology.
Concerns
The data-driven nature of AI implies that automotive solutions leveraging it should process large, high-quality datasets as well as streams of real-time information from relevant sources to perform analytical and operational tasks.
AI-powered solutions feature multi-layered architectures with interconnected components like IoT sensors and data analytics software, which can rely on different communication protocols to exchange multiple types of data. Poor interaction between such components results in information silos, inconsistent data, and unreliable analyses.
Recommendations
Configure ETL/ELT pipelines to integrate heterogeneous data, cleanse it, and consolidate it into a suitable data storage (data lake, TSDB, etc.). Cloud service providers offer cloud data integration solutions like Azure Data Factory and AWS Glue to facilitate this process.
Set up necessary application program interfaces (APIs) to enable seamless data exchange between components. Once again, you can rely on cloud-based services like Azure API Management and Amazon API Gateway.
Consider using middleware architectures like ESB to convert incompatible communication protocols, or data virtualization techniques to access data stored in other components without having to move or copy it.
Connectivity
Concerns
Several use cases of AI in the automotive industry involve data exchange between geographically distributed systems, including IoT sensors gathering vehicle information and data analytics solutions processing it. This requires very stable networks, especially when data processing needs to be done in real time, such as for route optimization.
Getting a reliable internet connection becomes even more difficult when experiencing load spikes or blind spots, such as tunnels, which can cause high latency and lead to inaccurate analytics.
Recommendations
Deploy IoT sensors loosely coupled to other components of your AI solution according to the publish-subscribe pattern. Such devices can store data in an offline message queue in the event of poor or no connectivity and transmit it once the connection is restored.
Adopt edge computing to distribute part of the computing workload (typically handled by centralized data centers) across IoT devices. This will help reduce network dependency and mitigate latency risk.
Rely on bandwidth-efficient communication protocols, such as MQTT.
Data analysis
Concerns
Processing big data and data streams gathered from manufacturing plants or fleets of vehicles requires remarkable computing resources, enabled by a robust but potentially pricey technology infrastructure.
Machine learning algorithms require massive training data sets to build an AI model capable of deriving reliable insights.
AI model reliability can be hindered by overfitting (a model overtrained on a specific data set performs poorly when processing other data) or model drift (its performance degrades over time due to progressive changes in input variables).
Recommendations
Complement your computing resources with cloud-based AI and ML services. These typically provide built-in algorithms, pre-trained ML models, and scalable processing power. Amazon SageMaker and Azure Machine Learning are popular options in this regard.
Implement a masterless cluster architecture to scale up and down the processing nodes supporting your IoT network according to the actual workload.
Split your data for AI model building into training, validation, and test sets to prevent overfitting. Regularly monitor model drift based on metrics like the Population Stability Index and execute retraining iterations to update the model with fresh data.
Data privacy & security
Concerns
As the volumes of data required by AI systems for training and analysis increase, so do security risks. Fraudsters and cybercriminals can target both private drivers and automakers, with threats ranging from car hacking to intellectual property leakage.
Most of the information processed by automotive AI solutions, such as video footage collected by dash cams or geolocation data, is classified as personal data. Current regulatory frameworks impose significant limitations on the collection of this information.
Recommendations
Automotive data should be processed in line with the GDPR and other applicable standards and legislation, so make sure your AI solution complies with such regulations.
Whenever possible, train your AI models with obfuscated data, namely data modified via data masking techniques like encryption to anonymize it.
Minimize cyber exposure of your AI system and related data by implementing effective security measures. These can include cryptographic key management systems exclusive to each vehicle, dynamic data masking for your databases, and device authentication options like X.509 certificates for your IoT sensors.
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Transforming the automotive industry with AI
The automotive industry is set to transform radically, thanks to advances in AI technology. From manufacturing and design to sales, marketing, and servicing, AI can play a key role in making cars smarter, safer, and more efficient. Additionally, the inevitable shift from hardware to software in the automotive industry requires vehicle manufacturers to reimagine their workflows and pay close attention to the relevant regulatory frameworks. If you are planning to implement a robust AI solution to embrace the numerous tangible benefits the technology offers, consider collaborating with an expert partner like Itransition.
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