The Influence of AI on the Future of Automotive Technology Development
Artificial Intelligence (AI) is revolutionizing numerous industries and significant transformation are occurring in the automotive sector. Thanks to advancements in machine learning, neural networks, and data analytics, AI is pushing change and enhancing the capabilities of vehicles globally. From autonomous driving to predictive maintenance, AI is at the forefront of creating a more integrated driving experience.
The issues most people have with AI is the rate at which its capabilities continually change. AI utilizes Large Language Models (LLMs) to develop knowledge on a variety of topics. These LLMs provide AI with a subset of information that it can use to make calculated decisions to determine the next possible outcome. This skill set of intelligence provides AI with the ability to predict the future based on the present and the past information in a specific situation. Mechanically, it makes sense, as a mechanical process will undoubtedly produce one of several potential outcomes, no matter what the input is. Providing a predictive outcome allows those that look at it to determine what potentially could happen if certain inputs are inputted.
Managing vehicle upkeep and control by a human can be trying at times because of the emotion factor playing into the decision processes. AI takes the emotion, favoritism and bias out of the decision process. Utilizing almost instantaneous information and then comparing that to known past information allows for the continuous improvement of AI’s decision process. Humans try to accomplish this through documentation and experiences, but the amount of this information is so vast a human must be constantly increasing their lived experiences to even get close to the decision processes that AI can quickly decide. Increasing use of AI will provide the automotive manufacture with the ability to increase the capability of the vehicle and provide for a better experience for the driver.
Autonomous Vehicles: The Future of Transportation
One of the most talked-about applications of AI in the automotive industry is the development of autonomous vehicles. These self-driving cars rely on complex algorithms, sensors, and real-time data processing to navigate and make decisions without human intervention. Autonomous vehicles can significantly reduce the number of traffic accidents caused by a distracted driver, which accounts for approximately 94% of all accidents (Smith et al., 2023). By leveraging AI, these vehicles can analyze vast amounts of data from their environment, detect potential hazards, and react faster than a human driver ever could. Increasing the rate at which transportation can decipher information will decrease the possibility of collisions and increase riders’ satisfaction. Increasing the ability of the vehicle to meet the requirements of actual autonomy is the ultimate goal.
Levels of Autonomy
The Society of Automotive Engineers (SAE) defines six levels of vehicle autonomy, ranging from Level 0 (no automation) to Level 5 (full automation) (Shuttleworth, 2019). Currently, the most commercially available autonomous vehicles are at Levels 2 and 3, which involve partial automation, where the driver must still be engaged and ready to take control if necessary.
Currently Mercedes Benz is the only manufacturer that is providing a full Level 3 autonomous vehicle on the road that doesn’t require the driver to have full attention on the roadway (Thubron, 2024). This program does require a $2,500 per year subscription cost, but it is available for those that have the funding to purchase this type of feature (Thubron, 2024).
Level 4 and Level 5 vehicles, which represent high and full automation respectively, are still under development. Companies like Tesla, Waymo, and Uber are at the forefront of this research, conducting extensive testing to bring fully autonomous vehicles to the market. There are several pilot projects that include full level 5 vehicles, such as the Waymo pilot in San Francisco (Waymo, 2025). Throughout all their pilot cities they have travelled over 25 million miles without a driver in a structured setting (Waymo, 2025). When I mention that structured setting it means that there is a set area that allows for its operation. This set area is laid out with Waymo and the local municipalities to provide some pattern to the system. Keeping the area sort of controlled allows the autonomy to test operational theories within that control area and provide use case analysis of the rides to provide improvement data to the operators. Turning this type of technology loose in the wild may provide information or situations that are not anticipated by the operational system. Working through the issues within a controlled environment will increase the rate of improvement of technology to a point where the situation will not be as relevant to operations.
AI in Vehicle Safety Systems
AI is also enhancing vehicle safety through advanced driver-assistance systems (ADAS). These systems utilize machine learning algorithms to improve vehicle safety and performance by monitoring the vehicle's surroundings and assisting the driver in various ways. From collision avoidance to Adaptive Cruise Control (ACC), the ability of AI to anticipate the operational habits of the driver and those vehicles around it increases the abilities of the vehicle to operate in concert or independently of the driver. By predicting what might happen the vehicle’s AI can provide a smoother acceleration or deceleration to make the operational environment more pleasurable to the occupants. Utilizing the various external systems present around the vehicle and integrating them into operations will further enhance how these systems operate.
Vehicle Operational Conditions
AI algorithms continuously monitor the vehicle's health by analyzing data from sensors embedded in critical components such as the engine, transmission, and brakes. This real-time monitoring allows for the early detection of anomalies and the prediction of component required maintenance schedules. Utilizing AI to help reduce unscheduled maintenance can decrease downtime and increase the life of the vehicle. Just like heavy truck fleet management software, fixing potential failures before they fail will decrease the time the operator is without a vehicle and less likely the vehicle will be out of commission.
This proactive approach not only enhances vehicle performance but also ensures a safer driving experience. Like any vehicle getting the driver/owner to perform the maintenance task, most preventative maintenance items are forgotten or ignored by the owner. OEMs are moving towards forcing the vehicle to derate to entice those owners to continually come in to service their vehicles to maintain the performance of that vehicle.
Governmental entities force owners to continually fix emissions and safety related issues yearly before they are allowed to drive the vehicle on the road will help to reinforce this habit of continually maintaining the vehicle. AI helps the owner by finding the local service facility, scheduling the appointment and even communicating ahead to the facility to indicate what may be the problem with the vehicle. Removing the human aspect of vehicle maintenance will continually increase the longevity of the vehicle. With vehicle prices rivaling the cost of housing in some places, a vehicle is becoming a decade or more investment in consumers’ life.
AI and Vehicle Personalization
AI is being utilized to create personalized driving experiences that cater to individual preferences and needs. Customizing the entertainment, environmental conditioning, and other vehicle features, the driver can quickly accustom themselves to the situation to meet their desires before and while driving. Millennial and Gen Z drivers are more attune to the Greenhouse Gas (GHG) emissions and are opting to select vehicles that meet those lower output models (Wang, 2024). This shift of owner and driver ship requires the OEMs to provide more information, features and mindfulness on how they personalize the vehicle. Connectivity with the environment around them as well as meeting the environmental needs of the next generation are both vital to keeping them as customers going forward.
This integration further into the life of the generation has resulted in the Software Defined Vehicle (SDV). An SDV can utilize AI in concert with vehicle operational software to customize it further than ever before. Personalizing the driving experience will increase satisfaction and decrease cognitive load on the driver. AI-powered driver assistance systems can adapt to the driver's behavior and preferences, offering personalized recommendations and assistance. AI can suggest the best routes based on the driver's driving history and real-time traffic data or adjust the seat and mirror positions to the driver's preferred settings. Integrating the cell phone and other mobile features into the ecosystem of the vehicle provides for a more immersive experience the Millennial and Gen Z are desiring.
AI in Electric Vehicles
AI is playing a larger role in Electric Vehicle (EV) operation and management. From managing how the vehicle operates as a SDV to managing the battery and high voltage systems, AI can increase longevity and performance. Predicting battery cell degradation, optimizing charging cycles based on vehicle location and providing home power to help offset high electricity costs, will become the way of society as we move forward. Vehicle to Home (V2H) technologies are adding a feature that was not available in conventionally powered vehicles. Optimizing various electrical inputs from grid, solar, wind and other power generation sources, the EV has the ability to decrease the cost of living based on capturing electricity at lower rate times instead of being at the mercy of the electrical provider (Wu et al., 2024). Energy efficiency of the vehicle and vehicles battery pack can be increased in the future by AI to optimize motor generator usage through all levels of operation. AI can also help increase the life of the battery cell present in the vehicle.
Controlling battery cell operation through the Battery Management System (BMS) and utilizing Electrochemcial Impedance Spectroscopy (EIS), AI deep learning can predict when cells need attention before they are physically showing signs of failure (Palanichamy & Soni, 2024). This deep learning strategy will increase the life along with increasing the performance of the battery pack throughout the operational range of the vehicle. This increase in usage will help to offset the change from ICE powered vehicles to those powered by a battery pack. Getting the consumer to feel comfortable within the vehicle will increase the adoption of this technology and help power the transition from one form of propulsion to another.
Challenges with AI Integration
While integrating AI into the EV operational systems it can cause unknown situations that will need to be mitigated as they start to appear. The ethical dilemma will be at the top of the challenge list as how do you determine what human decisions should be made by AI? Life – threatening situations may occur and is AI best equipped to decide who lives and who dies? The decision process for non-standard situations must be studied and should be written into the code for use within operational environments.
Another issue with AI integration is the amount of data that is utilized to allow that AI to make a decision. These data points will continue to raise concerns about data privacy and cybersecurity of the system. Receiving compromised data will cause the AI to make decisions on falsehoods, which could lead to a wrong decision being made. As the collection of the varying data sets grow, the privacy of your information will increasingly become an issue as AI will demand a larger and larger subset of information to further enhance its decision process. Ensuring that personal and sensitive information is protected is a priority for both manufacturers and regulators. This includes anonymizing data, securing data transmission, and providing users with control over their information.
Conclusion
Artificial Intelligence is changing the automotive industry, offering extraordinary opportunities for innovation and advancement. From autonomous driving and vehicle safety to predictive maintenance and personalization, AI is enhancing every aspect of the automotive experience. Nevertheless, it is essential to address the challenges associated with AI, including ethical considerations, data privacy, and regulatory frameworks. By doing so, we can ensure that the benefits of AI are realized while minimizing potential risks.
As AI technology continues to evolve, the automotive industry must be continually advancing the training to the next level of evolution. Adopting AI will undoubtedly lead to safer, more effective, and more enjoyable driving experiences, paving the way for a future where technology and transportation are seamlessly integrated. As technicians in this changing environment, they must be able to adapt to this ever-changing environment. Adaptation requires a thorough understanding of the concepts and applications related to those concepts; continuous learning is vital to the further development of these mobility technicians into pseudo computer mechanical technologists. Incorporating this information into any automotive training program is a crucial next step in developing the pipeline of technicians.
Keeping technology at the forefront of the training provides those students with the needs of industry as it transitions to a more integrated situation with the help of AI. AI will not replace the technicians in this profession, rather it will provide those technicians with tools to accelerate their diagnosis and proper diagnosis of failures within the vehicle. Embracing this change will differentiate the true technicians from those that just are going through the motions.
The MAST series of CDX provides the instructor with pointed material to exceed the requirements of any ASE training currently on the market. Utilizing the Read-See-Do model throughout the series, the student has various learning modalities present throughout the products which allow them to pick the way they learn the best. From developing simulations on cutting edge topics to providing a depth of automotive technical background, CDX has a commitment to making sure instructors and students have the relevant training material to further hone their skill sets within the mechanical, electrical and software driven repair industry. CDX Learning Systems offers a growing library of automotive content that brings highly technical content to the classroom to keep you and your students up to date on what is currently happening within the Mobility Industry. Check out our Light Duty Hybrid and Electric Vehicles, along with our complete catalog Here.
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About the Author
Nicholas Goodnight, PhD is an ASE Master Certified Automotive and Truck Technician and an Instructor at Ivy Tech Community College. With nearly 20 years of industry experience, he brings his passion and expertise to teaching college students the workplace skills they need on the job. For the last several years, Dr. Goodnight has taught in his local community of Fort Wayne and enjoys helping others succeed in their desire to become automotive technicians. He is also the author of many CDX Learning Systems textbooks, including Light Duty Hybrid and Electric Vehicles (2023), Automotive Engine Performance (2020), Automotive Braking Systems (20219), and Automotive Engine Repair (2018).
References
Palanichamy, K., & Soni, J. (2024). Optimizing Electric Vehicle Performance With AI-Driven Battery Management Systems. Educational Administration Theory and Practices. https://doi.org/10.53555/kuey.v30i3.6796
Shuttleworth, J. (2019). SAE Standards News: J3016 automated-driving graphic update. https://www.sae.org/news/2019/01/sae-updates-j3016-automated-driving-graphic
Thubron, R. (2024, April 23). Mercedes-Benz becomes the first automaker to sell Level 3 autonomous vehicles in the US. Techspot. https://www.techspot.com/news/102705-mercedes-benz-launches-first-level-3-autonomous-vehicles.html
Wang, K. (2024). Are Generation Z Less Car-centric Than Millennials? A Nationwide Analysis Through the Lens of Youth Licensing. https://www.sciencedirect.com/science/article/abs/pii/S0264275124001653
Waymo. (2025, February 2). Waymo. Waymo. https://waymo.com/about/#story
Wu, Y., Aziz, S. M., & Haque, M. H. (2024). Vehicle-to-home operation and multi-location charging of electric vehicles for energy cost optimization of households with photovoltaic system and battery energy storage. Renewable Energy, 221. https://doi.org/10.1016/j.renene.2023.119729