AI in the Automotive Industry: Advancements & Dilemmas

Classification of Autonomous Vehicles

Before diving into the technological rabbit hole, it is important to establish a good foundation of knowledge to grasp the full extent of autonomous cars. The levels of autonomy range from 0 with no automation to 5 with fully automated systems. It Is important to acknowledge that levels 4 and 5 haven't been fully realized yet as they are still under development.

Level 0

This level indicates no automation, which describes traditional cars that leave the driver to perform all driving tasks. The vehicle may have features, such as basic warnings, But the brunt of the work is left to the driver.

Level 1

This is the most basic level of automation, which typically offers no more than assistance to drivers, leaving them fully in control of the car. This tier usually manifests in the form of adaptive cruise control, where the vehicle can be kept at the appropriate distance behind the next car, and lane-keeping assistance, which helps vehicles stay in the same lane.

Level 2

This level is standard in many luxury vehicles today, engaging both brakes and acceleration support. A good example of this is the General Motors Super Cruise system, which combines advanced adaptive cruise control with lane-centering technology to allow vehicles to take control of braking and acceleration/deceleration on highways. A prominent example includes Tesla’s autopilot function.

Level 3

Also known as conditional automation, the third level allows you to be the copilot with the vehicle’s AI, which can handle traffic congestion, highway driving, and other very specific scenarios. However, there are limitations to this level of self-driving capabilities, as you will be expected to take over when the car's AI indicates that it needs assistance. Audi’s traffic jam pilot and Mercedes Benz’s drive pilot represent very good examples of this.

Level 4

Currently, this level hasn’t been fully achieved as it is still under development and requires a lot of testing. This high automation tier takes the responsibility of controlling the car off the human driver’s hands when driving within covered virtual geographical areas, also known as geo-fenced areas. Outside such geo-fenced areas and in certain weather conditions, the AI can’t be used, and the driver may need to retake control of the vehicle.

Companies such as Waymo LLC (formerly Google’s self-driving car project), which operates in San Francisco and Phoenix in the United States, as well as the French company Navya, serve as notable instances of this level of autonomy.

Level 5

At the highest level, vehicles are fully autonomous. In theory, these types of vehicles can perform all driving tasks in all situations and areas, even in locations that haven’t been carefully mapped out, providing you with the best driving experience many have dreamed of. Currently, these advanced cars don't exist, and many speculate that they may never exist, but we learned from the past that a single ground-breaking advancement could unlock unlimited possibilities.

AI Advancements in the Automotive Industry

Even though we’re still witnessing the early stages of AI, its impact on the automotive industry is already significant, ushering in changes across design philosophy, operation, manufacturing, and customer experience. Companies are using AI to optimize decision-making, giving them a competitive advantage through timely responses and ultimately providing a better customer experience. Take General Motors, for instance, which uses AI to develop prototypes for their vehicles. They’ve utilized it by developing a new seat belt bracket that is 40 percent lighter and 20 percent stronger than their original design.

With this highly competitive change of pace, companies are forced to switch their plan-setting strategies from annual or quarterly to more immediate, real-time plans that aim to incorporate market trends and damage control. This ultimately aids automobile manufacturers in developing strategies to prevent any potential mishaps that individual vehicle owners may face.

Numerous car dealerships have embraced AI technology. A survey done by CDK Global Inc. indicates that 76% of dealers attribute their efficient and quick operations to AI. If you’re looking to buy a car, you might want to find a dealership that uses AI for the best customer experience. Unsure where to start? You can visit our list of the best car dealerships in 2023, which tells you everything you need to know about a wide array of different dealerships.

Predictive maintenance

As one of the most transformative forms of artificial intelligence in the automotive industry, it stands out due to its unique approach to problem-solving. Instead of simply troubleshooting problems, predictive AI maintenance relies on IoT (internet of things) sensors, which analyze data and use AI algorithms to predict when a component is likely to fail. It works by studying a massive volume of data supplied by sensors, telematics systems, and maintenance logs that can detect potential issues. This effective tool reduces downtime and enhances the safety net already in place.

Natural language processing

Natural language processing (NLP) is a branch of AI that studies the interaction between computers and humans through natural language, allowing drivers and passengers to communicate with their vehicle using everyday terms and phrases. Drivers can simply verbally request various functions, such as selecting a destination, changing the song, or even adjusting the air conditioning. NLP is not just about understanding spoken words; it's about grasping the context and tone.

Personal voice assistants such as Siri, Alexa, and Bixby have become a distinguishing feature of NLP over the last decade. Their successors are gradually merging into vehicles, offering a wide array of services that mirror those we are accustomed to in other aspects of our lives. Instead of managing appointments and ordering groceries, in the automotive context, assistants are able to provide turn-by-turn navigation, read aloud text messages, and play music. All while the driver's hands remain on the steering wheel.

Autonomous driving and safety measures

As cars integrate AI elements, drivers become more accustomed to AI-infused advanced driver-assistance systems (ADAS), such as lane departure warning (LDW), autonomous emergency braking (AEB), and adaptive cruise control (ACC). AI technology uses sensors and cameras to detect potential hazards in real time; this is a major mechanism that enables automatic brake control.  These advancements are not limited to driving, as there are plans to use the wealth of data AI accumulates to revolutionize the auto warranty industry through automated claims and risk assessment processes. 

Digital twins

Within the automotive industry, a groundbreaking concept is gaining traction, promising substantial cost savings and accelerated testing of components integral to vehicle manufacturing. The emergence of “digital twins” is unlocking many opportunities for cost savings and efficient testing beyond what we know today. At its core, the concept of digital twins involves the creation of a digital replica that mirrors every aspect of the vehicle's composition, interactions, and behavior.

There are several types of digital twins, each serving a specific purpose. The four main types include component twins, asset twins, system twins, and process twins. Component twins aim to represent, analyze, and identify the performance and efficiencies of individual parts of a larger system. Asset twins take component twins to the next level and represent entire assets such as machines, vehicles, or buildings. They are used to identify possible issues and optimize maintenance. System twins represent whole systems, such as manufacturing lines for energy storage systems, and are used to streamline and improve the system as a whole. Lastly, process twins are used to replicate and enhance a specific process in a system, such as a supply chain.

With this technology, auto warranty companies have the potential to personalize services, simulate various scenarios, and monitor a vehicle’s performance in real time to identify potential issues and predict maintenance needs to devise predictive maintenance strategies.

Major industry players like BMW have already embraced digital twins to identify problems and simulate solutions before producing real parts. These car manufacturers are streamlining processes and reducing both time and costs by omitting the traditional prototyping phase.

How Can AI Improve the Auto Warranty Industry?

The standard claims systems that most warranty companies currently use are slow, prone to error, and can't do much outside of “if” commands. These outdated systems usually pose significant challenges because they are costly and difficult to maintain and update.

To improve their data processing systems and enhance their customer experience, these companies require the capacity to pour huge volumes of real-time data into an intelligent computer system that can improve risk awareness and mitigation, decipher patterns, and provide insight.

Implementing comprehensive AI systems that predict maintenance requirements and address them swiftly through pattern-finding technologies can be a great boon to auto warranty companies that preach vehicle safety and peace of mind. They can also develop AI and ML (machine learning) models to evaluate and identify trends that can aid their teams in finding issues many months before they affect customers.

Such systems can rank suppliers based on their parts quality, failure rate, and recovery rate. AI could single out oddities and identify fraudulent activity based on this ranking system and the patterns found within it.

Even though AI hasn't made its way into the auto warranty industry, if you're on the hunt for the right extended auto warranty, you can check out our list of the best extended warranty companies. We've done the research for you, gathering a wide range of options and providing straightforward reviews to help you find what you need.

Moral Dilemmas of Autonomous Cars

In the realm of artificial intelligence, self-driving vehicles and their implications stand as largely uncharted territory. While they might appear as a solution to road safety, upon further study, it becomes clear that a system relying solely on machine learning is susceptible to bugs and hacking. This creates the potential for bigger, more destructive hazards on the road than what we already have with hands-on driving. And that's just the tip of the iceberg; there are many ethical issues waiting to be discovered, such as liability, job displacement, and data security.

Liability

Accidents are an unwelcome companion on the road, and often the driver bears the brunt of responsibility. But if we have AI driving the car, who would take responsibility? Should it fall in the hands of the manufacturer or the human user, who could have intervened?

Job displacement

Many of today’s workers have a driving-related job; in fact, there are over 2,350,464 drivers currently employed in the US alone. The adoption of AI might lead to the displacement of most, if not all, of these drivers; therefore, we must look at what the convenience of AI will truly cost us at the end of the day.

Data security

AI-operated cars are data magnets, capturing all aspects of our lives down to the last detail, from behavioral patterns to preferences. However, it's important to ask the question, Who’s in charge, and what do they intend to do with such a wealth of information?

Navigating the intricacies of consent and privacy preservation has been hard with the emergence of the internet and is becoming even more complex with the evolution of AI, especially in the automotive industry.

Concluding Thoughts

The integration of AI into the automotive industry is shaping vehicle production and auto warranty standards as a whole, promising safety and comfort. Although we’re in the early stages of integrating AI into our lives, it has already proven to be revolutionary as systems such as Digital Twins, predictive maintenance, and natural language processing have been made possible. And that's not where it ends; auto warranty companies are looking to use machine learning to improve risk awareness and mitigation, detect fraud, and improve claims to provide a well-rounded customer experience.

However, upon taking a closer look, we come to realize that there's more to it than meets the eye. AI’s potential to reduce accidents is offset by its vulnerability to bugs and hacking. Ethical dilemmas such as liability allocation, job displacement, and data security add to the continuously building web of complexities. Ultimately, these setbacks do not diminish the invaluable wealth of data that AI is accumulating with the goal of enhancing vehicle safety, streamlining operations, and identifying potential errors.

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