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1 Introduction

2 from hardware-defined to software-defined vehicles, 3 digitalization of the value chain, 4 human-centered autonomous systems design, 5 closing thoughts, conflict of interest, data availability statement, design of next-generation automotive systems: challenges and research opportunities.

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Panchal, J. H., and Wang, Z. (August 25, 2023). "Design of Next-Generation Automotive Systems: Challenges and Research Opportunities." ASME. J. Comput. Inf. Sci. Eng . December 2023; 23(6): 060818. https://doi.org/10.1115/1.4063067

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The automotive industry is undergoing a massive transformation, driven by the mega-trends of “CASE”: connected, autonomous, shared, and electric. These trends are affecting the nature of automobiles, both internally and externally. Internally, the transition from internal combustion engines (ICE) to electric drive-trains has resulted in a shift from hardware-centric vehicles to software-defined vehicles (SDVs), where software is increasingly becoming the dominant asset in the automotive value chain. These trends are leading to new design challenges such as how to manage different configurations of design, how to decouple the design of software and services from hardware, and how to design hardware to allow for upgrades. Externally, automobiles are no longer isolated products. Instead, they are part of the larger digital ecosystem with cloud connectivity. Vehicle usage data are increasingly connected with smart factories, which create new opportunities for agile product development and mass customization of features. The role of the human driver is also changing with increasing levels of autonomy features. In this paper, the authors discuss the ongoing transformation in the automotive industry and its implications for engineering design. The paper presents a road map for engineering design research for next-generation automotive applications.

The automotive industry is currently undergoing a massive transformation, driven by emerging energy and information technologies, as well as a changing environmental landscape. Key trends, such as electrification, connectivity, digitalization, and autonomy, are shaping the design and production of next-generation vehicles. Automotive manufacturers are focusing on electric powertrains and connected vehicles, striving for carbon neutrality by 2050 [ 1 ], and emphasizing the importance of ongoing software updates, information security, and privacy [ 2 , 3 ].

Electrification and the shift toward carbon neutrality are driving the adoption of alternative power sources such as electric power and hydrogen fuel cells, along with new materials and manufacturing processes that minimize the environmental impact. As vehicles become more connected, they can communicate with the cloud, other vehicles, and infrastructure, enabling real-time data sharing and improving traffic management. This connectivity, combined with the digitalization of the automotive value chain, is replacing mechanical complexity with software complexity, leading to shorter time-to-market and increased focus on dependability, safety, security, and privacy.

The rise of autonomous driving is expected to revolutionize the industry by improving road safety, reducing congestion, and increasing efficiency. As self-driving technology matures, automakers are competing to develop and deploy autonomous vehicles that meet regulatory standards and consumer expectations. Simultaneously, the increasing availability and integration of data across various platforms are playing a crucial role in the development of advanced automotive systems. This trend necessitates robust measures to protect sensitive information and address concerns about data privacy and security. Additionally, changing consumer preferences and the rise of ride-sharing platforms are leading to a shift away from individual car ownership. This trend is driving the development of new mobility solutions, such as car-sharing and subscription services, which offer more flexible and cost-effective transportation options for the consumers. These trends in the automotive industry pose new challenges in the design and operation of automotive systems.

From a design standpoint , these challenges encompass the integration of design tools, processes, and practices, along with the coordination of multi-disciplinary entities within organizations. Furthermore, the integration of suppliers within the supply chain adds an additional layer of complexity to the design process.

From an operations perspective , systems integration challenges are multifaceted. Vehicle-to-driver integration demands seamless communication between the vehicle’s systems and the driver, ensuring safety, efficiency, and an intuitive user experience. Integration of vehicles with digital infrastructure, including data, models, and cloud services, is crucial for enabling real-time data sharing, remote software updates, and predictive maintenance. This requires robust systems and secure communication protocols to protect sensitive information and maintain privacy. Vehicle-to-vehicle integration enables cooperative driving, allowing vehicles to share information about their position, speed, and other parameters to improve traffic flow, safety, and energy efficiency. This form of integration relies on the development and standardization of communication protocols to ensure interoperability between vehicles from different manufacturers. Lastly, vehicle-to-road infrastructure integration requires embedding sensors and communication devices in roads, traffic lights, and other infrastructure elements to facilitate information exchange between vehicles and their surroundings, optimizing traffic management and enhancing safety.

Transition from Hardware-Defined to Software-Defined Vehicles: As vehicles become increasingly software-driven, the engineering design and systems engineering methodologies, tools, and processes must be adapted. Research opportunities in this area include the development of modular software architectures, efficient integration of software and hardware components, and the management of growing software complexity.

Digitalization of the Value Chain: The digitalization of the automotive value chain presents numerous research opportunities in areas of supply chain management, manufacturing, and product life cycle management. This includes the exploration of digital twins, virtual prototyping, and the use of artificial intelligence for optimization and predictive analytics. Furthermore, the integration of suppliers, multi-disciplinary entities, and other stakeholders in the digital value chain necessitates the development of new collaborative tools and frameworks.

Human-Centered Autonomous Systems Design: As autonomous systems become more prevalent in the automotive industry and beyond, it is essential to maintain a human-centered approach to design. Research opportunities in this domain encompass the development of intuitive human-machine interfaces, the investigation of user trust and acceptance of autonomous systems, and the consideration of ethical and societal implications of autonomous vehicles. Additionally, there are new challenges in how to effectively integrate autonomous systems with existing infrastructure and ensure the safety and reliability of these systems in diverse and complex environments.

A summary of the challenges and research opportunities is presented in Table 1 . By leveraging these research opportunities, the engineering design community can contribute to the successful evolution of the automotive industry in response to emerging trends. The paper is structured as follows. Section 2 discusses the implications of the shift from hardware-defined to software-defined vehicles (SDVs) for engineering systems design research. Section 3 focuses on the digitalization of the value chain, and the corresponding challenges for design research. Section 4 discusses the challenges and research opportunities associated with human-centered autonomous systems design. Finally, Sec. 5 concludes the paper by highlighting the need for a holistic cyber-physical-human view of automotive systems integration.

Summary of challenges and research opportunities

2.1 Trends.

Traditionally, automobiles were hardware-centric, with their architecture defined by mechanical subsystems such as the body, chassis, powertrain, fuel system, electrical system, climate control system, safety and security system, driver interface, and infotainment system [ 4 ]. Over time, software and electronics began to play an increasingly significant role in vehicles, evolving from simple engine controllers to sophisticated infotainment systems, and from passive safety features to advanced driver assistance systems (ADAS). Increasingly, vehicles are becoming more software-defined, with embedded systems and connected devices facilitating communication between various subsystems and enabling a host of new features and functionalities. Examples of software and electronics integration in modern vehicles include engine control units (ECUs) that optimize engine performance & fuel efficiency, advanced safety features such as adaptive cruise control & automatic emergency braking, and connected infotainment systems that provide navigation, entertainment, and real-time traffic updates. This shift toward software and electronics has transformed the design of automotive systems, leading to the development of innovative technologies that improve vehicle performance, safety, and user experience.

Following the tradition of hardware-centric design, the current paradigm involves tightly integrated hardware and software development at the sub-system level. In this paradigm, software and hardware are treated in the same way, resulting in a highly coupled development process. Software release cycles are closely tied to hardware releases. This focus on hardware development, along with the growing significance of software, is reflected in engineering design and systems engineering processes [ 4 ]. The systems engineering Vee model, a well-known model of systems development, emphasizes parallel development and integration of hardware and software components, ensuring that both aspects are considered throughout the entire design process.

Continuous functional updates: The ability to provide ongoing software updates not only enhances the vehicle’s performance but also reduces time to market for new functions.

Software longevity: Unlike hardware components that can wear down over time, software does not age, allowing for consistent and reliable performance throughout the vehicle’s lifecycle.

Economical advantages: The cost of the software bill of materials is essentially $0, enabling cost-effective updates and improvements without incurring additional expenses associated with hardware components.

This shift from hardware-defined to software-defined vehicles has significant implications for engineering design and systems engineering, necessitating the adoption of new methodologies, tools, and processes that can effectively address the challenges and opportunities presented by the increasing reliance on software. This shift enables automakers to focus on a different set of design objectives . Instead of designing for a fixed set of requirements for a market segment, the focus is on creating platforms that can continuously evolve. As a result, the old paradigm of a vehicle never being better than when it is new is giving way to the new paradigm of the vehicle evolving and improving over time. This shift emphasizes adaptability over robustness, requiring engineers and designers to rethink their approach to vehicle design.

Analogously, significantly greater amount of personalization is feasible with software-defined architectures. Vehicles can learn and adapt to the evolving preferences of their users, providing a more tailored and engaging driving experience. This level of personalization requires the development of new models for capturing designers’ preferences and understanding the factors that drive customer satisfaction. Moreover, engineers and designers must determine the optimal timing for introducing new functionalities and establishing appropriate pricing strategies for these additional features. By embracing these changes in design objectives and focusing on adaptability and personalization, automotive designers can better respond to the shifting needs and expectations of consumers. This approach will enable the development of vehicles that not only enhance the driving experience but also evolve alongside their users, ensuring long-term satisfaction and value.

2.2 Research Challenges.

From Hardware Safety to Software Safety: Traditionally, vehicle design primarily involves the development of mechanical systems, such as engines, brakes, and steering. Machinery safety can be guaranteed with physical human-machine interfaces, such as manual switches or “big red buttons.” However, with the transition to software-defined vehicles, ensuring software safety is just as important as ensuring hardware safety. Research challenges emerge regarding developing new software engineering techniques and tools to ensure that the software used in vehicles is reliable, secure, and free from defects that could lead to accidents. Additionally, researchers should explore ways to integrate software and hardware safety systems to create a holistic approach to vehicle safety.

Complexity of the System Design: Due to the shift of focus from hardware design to software design, the complexity of vehicle systems changes dramatically given the dynamic nature of software systems. For example, compared to a traditional internal combustion engine (ICE) that has hundreds of moving parts, the drivetrain of an electric vehicle only has a handful of them. However, most electric vehicles nowadays have far more software than ICE vehicles, leading to different development timescales and life cycles. Complexity of the system design emerges with the interdependence between hardware and software on vehicles, and may further increase along with the software evolution with time. Therefore, research challenges arise regarding: (a) how to manage different configurations of design, (b) how to decouple the design of software and services from hardware, and (c) how to design hardware to allow for software-driven upgrades?

There is a need for research on holistic changes in automotive systems, processes, and organizations, which are difficult to conduct in real organizational settings. Consider the example of integration of the design tools, processes, and practices. Iterative and agile software development practices are replacing the stage-gate based development approaches. Automotive design processes are embracing DevOps models which are common in software development, where instead of viewing development and operations as distinct process steps, development and deployment are part of a continuum. Conventional processes typically concentrate on organizing substantial rollouts, which carry higher risk due to the extensive work they entail. On the other hand, DevOps operates with incremental launches, which are usually simpler to execute and examine, thereby significantly reducing the associated risks. Furthermore, any unanticipated outcomes can be easily rectified.

Traditional systems engineering and design processes are not adequate for designing such next-generation automotive systems. Fundamental research is needed to understand the effects of different types of integration of mechanical and software design practices on the product outcomes (e.g., performance, quality, and reliability) and process outcomes (e.g., time to market). Other research opportunities include evaluation of the effects of different vehicle architectures, rapid exploration and analysis of architectural changes, and new technology insertion.

Commonality, Modularity, and Reusability: In traditional vehicle design, commonality and modularity were achieved through the use of standardized mechanical components that could be easily interchanged between different models or manufacturers. Therefore, to achieve commonality and modularity in software-defined vehicles, the design process of software architectures also needs to be compatible across various vehicle models and platforms. Additionally, ensuring the reusability of software modules requires careful consideration of software design principles that make them modular, portable, and scalable. Addressing these challenges will require the development of new software engineering techniques and tools to support the creation of standardized and reusable software architectures for software-defined vehicles.

Several organizations are collaborating toward reusable standardized components for software-defined vehicles. For example, Continental and Amazon Web Services (AWS) have jointly developed a modular hardware and software platform called continental automotive edge (CAEdge), which provides a development environment for vehicle manufacturers to connect vehicles to the cloud, and implement software, sensor, and big data solutions efficiently and securely [ 8 ]. Another example is the automotive open system architecture, which is a global industry partnership in the automotive and software industry to develop and establish the standardized software framework for intelligent mobility [ 9 ].

Socio-Technical Aspects: The design of software-defined vehicles requires new organizational structures that co-evolve with software systems. For newer companies that are built from scratch around software-defined vehicle architectures, this can be easier than those older companies that possess a significant legacy of traditional vehicle architectures. Furthermore, the design of software-defined vehicles requires a balance between different cultures. Silicon Valley’s culture emphasizes moving fast and breaking things, while traditional automotive industry emphasizes rigorous testing and evaluation before product release. The former might not be suitable for designing software that is safety-critical, while the latter might not be suitable for designing software that needs frequent updates. Socio-technical research in integrated design processes for vehicle software and hardware are needed to find the balance between these two approaches.

Digitalization of the value chain in the automotive industry is revolutionizing operations, with companies like BMW leveraging digital technologies and data analytics to optimize processes and enhance customer experiences. BMW, for example, is working toward democratizing data availability throughout the organization by implementing initiatives such as the BMW cloud datahub [ 10 ], allowing employees throughout the organization to access, analyze, and share data easily. Organizations offer services and apps connecting vehicles to digital services, enhancing the driving experience and generating valuable data for further innovation. Collaborative innovation infrastructure is also being developed to support data exchange across organizational boundaries. For example, Gaia-X [ 11 ] is an open data framework promoting transparency, data sovereignty, and secure data exchange. By integrating data from various aspects of the business, such as manufacturing, logistics, customer service, and after-sales, organizations can optimize processes and streamline operations. The extensive use of data throughout the value chain has also opened new monetization opportunities for the automotive industry. Data-driven services, such as predictive maintenance [ 12 ], personalized recommendations [ 13 ], and usage-based insurance [ 14 ], generate additional revenue streams while enhancing the customer experience.

3.1 Trends.

Data within the vehicle: This type of data includes condition monitoring for on-demand maintenance, usage data for mass personalization, and vehicle performance data. These data help improve vehicle performance, tailor user experiences, and ensure timely maintenance to reduce overall costs.

Data outside the vehicle: Data from sources outside the vehicle include cloud connectivity, which enables simulation and validation functions and connects the vehicle with users’ other devices. This information helps enhance the driving experience, provide real-time updates, and offer seamless integration with the user’s digital ecosystem. Data from external sources like traffic and weather updates can be incorporated into the vehicle’s systems, providing drivers with real-time information and enhancing the overall driving experience.

Data across the organization: There is a wealth of data generated across different departments within an automotive company. In the factory, Industry 4.0 and digital thread technologies optimize manufacturing processes. Data generated by various departments, such as sales and marketing, finance, and supply chain management, can be integrated and analyzed to uncover insights, drive decision-making, and enable cross-functional collaboration.

The effective utilization of diverse data sources within and outside the vehicle, as well as across the organization, has the potential to transform the automotive industry.

3.2 Research Challenges.

Integration of a user’s digital life: One of the key challenges is to ensure a seamless integration of a user’s digital life into the automobile. This means that the user interface needs to be intuitive and the transition between different digital platforms (e.g., smartphone, smart home, vehicle) should be smooth. Research is needed to understand how to best design these interfaces and create seamless user experiences. As automobiles become more connected, they will generate and have access to more personal data, which raises concerns about data privacy and security. There is a need for research on developing robust methods for securing this data and protecting user privacy, while still allowing for personalized experiences. As technology evolves, so to do customer preferences. Research is needed to understand these evolving preferences and how they might impact the design of future automobiles. There is a tension between providing a personalized experience for each user and maintaining a standardized interface that is easy to understand and use. Finding the right balance between the goals of personalization and standardization is an important research opportunity. Technology and digital preferences evolve quickly, and a car’s lifespan is typically much longer than that of a digital device. Thus, there is a need to develop methods to design automotive systems that can adapt and remain current over time, such as through over-the-air updates or modular hardware design.

Digital twins: A digital twin is a virtual representation of a physical object or system [ 15 ]. In the context of vehicles, a digital twin can model the entire vehicle or specific subsystems. This can be particularly useful for condition-based monitoring, as it allows for real-time monitoring of the vehicle’s condition and performance, and can help to predict and diagnose issues before they lead to failures. Digital twins can also be used in the design and testing of new vehicles. By simulating the vehicle and its subsystems in a virtual environment, engineers can test new designs, identify potential issues, and optimize performance.

Creating and maintaining a digital twin requires significant computational resources and expertise in data analysis and simulation. It also raises issues around data privacy and security, as the digital twin needs access to detailed information about the vehicle and its usage. Research is needed to develop more efficient and effective methods for creating and using digital twins, and to address the associated privacy and security concerns. This could involve the development of new simulation techniques, machine learning algorithms for predictive maintenance, and cybersecurity measures to protect the data used by the digital twin.

Security of design and manufacturing: There is an increasing need for secure computing environments that not only protect sensitive data but also ensure the integrity of design and manufacturing processes. Research is required to identify potential vulnerabilities and to develop effective cybersecurity measures. These could include encryption, access control, intrusion detection, and secure software development practices. The security of design and manufacturing is an active research area with a wide range of initiatives such as development of attack taxonomies [ 16 ], methods for secure collaborative design [ 17 ], and secure information embedding in manufactured parts [ 18 ]. The traceability of parts throughout the supply chain is another crucial aspect of modern automotive manufacturing. It helps ensure the authenticity of parts, prevent counterfeiting, and provide accountability in case of failures. There is a need for research in developing more efficient and secure methods for manufacturing and parts traceability.

Standards organizations such as the National Institute of Standards and Technology (NIST) and American National Standards Institute (ANSI) are developing security models for additive manufacturing, addressing issues such as IP protection, tamper detection, and verification of part authenticity [ 19 ]. However, further research is needed to adapt these models to the specific needs of automotive manufacturing, and to keep pace with evolving technologies and threats. There is also a need for research environments that can simulate the complex ecosystems of automotive design and manufacturing, allowing researchers to identify potential attack vectors and simulate the effects of cyber-attacks. These environments could also be used to test new cybersecurity measures and to train personnel in cybersecurity practices. Such research environments can take various forms—a physical lab with various hardware and software components, a fully virtual environment, or a hybrid of the two.

Human-centered design (HCD) is an approach that prioritizes the needs, preferences, and abilities of the end users of a product or service. In automotive systems, HCD aims to create vehicles that are safe, comfortable, and enjoyable for drivers, passengers, and other road users. HCD involves various methods and tools, such as user research, prototyping, testing, and evaluation, to understand users and their contexts, and to design solutions that meet their expectations and goals [ 20 ].

4.1 Trends.

Ever since the DARPA Grand Challenge in 2005, where team Stanley led by Stanford University won the challenge by allowing their autonomous vehicle to navigate through a 142-mile long course through the Mojave desert in less than 7 h without manual intervention, the autonomous driving technology became increasingly popular to not only researchers but also the general public [ 21 ]. Following the success of Stanley, Sebastian Thrun from Stanford University launched the self-driving project at Google in 2009, which was later renamed Waymo in 2016 and has continuously led the technology development in the autonomous driving industry [ 22 ]. Along with these cutting-edge technology improvements in autonomous driving, rapid progresses in less advanced but more stable technologies have also been witnessed in the automotive industry, where ADAS are becoming standard features on mass-produced vehicles.

In the context of autonomous systems in the automotive domain, such as autonomous driving, HCD aims to design vehicle features that are safe, comfortable, and enjoyable to drive and ride. HCD involves understanding the human factors that affect driving behavior, such as perception, cognition, emotion, and social interaction [ 23 ]. It also involves designing and testing solutions that address these factors and enhance the user experience, which can facilitate autonomous driving to achieve its potential benefits, such as reducing traffic accidents, improving mobility, and saving time and energy.

With the emerging trend of software-defined vehicles, where vehicles can be dynamically configured and controlled by software, a high degree of flexibility and adaptability can be enabled for automotive systems. Autonomous features that were originally designed for mass-produced vehicles with generic settings, such as adaptive cruise control, can now be designed in a human-centered fashion by learning from personalized data sets generated by specific drivers [ 24 ]. Such personalized systems do not require any hardware modifications of vehicles, which can be agilely deployed by over-the-air (OTA) software updates with the help from internet of things (IoT) and/or digital twins [ 25 ].

However, the involvement of HCD in automotive systems also poses significant challenges for ensuring that the software behaves in a predictable and reliable manner, and that the human users can understand and interact with the system effectively. The following subsection presents an overview of the main research challenges faced in the HCD process of autonomous systems on vehicles, and discusses how they can potentially be addressed by advanced technologies.

4.2 Research Challenges.

Driver Distraction by Auxiliary Information: Autonomous systems on vehicles with HCD can create an immersive driving experience for drivers by leveraging auxiliary sources of information. For example, augmented reality-based head-up display overlays the guidance information to vehicle drivers on the windshield as an ADAS feature [ 26 ]. As ADAS still depend on human drivers to fully or partially operate vehicles, it is crucial to prevent drivers from being overwhelmed with too much information. Currently, ADAS employ the dashboard or multi-media screen to display information to drivers, requiring them to divert their attention from the road. With the rise of large touch screens in new vehicle models, driver distraction is further exacerbated, particularly with ADAS that require multiple touches to adjust settings. Augmented reality-based head-up displays offer a more intuitive way to present information to drivers, but they must be designed carefully to distinguish between real and projected objects.

Model Fidelity for Human Users: HCD for system design on vehicles can benefit greatly from big data derived from various sources, enabling them to make more informed guidance to vehicle occupants. However, simply having more data does not necessarily lead to better models, as not all data sources carry equal importance. For instance, historical driving data from a month ago cannot be treated the same as that from a day ago, as driver performance can change significantly over time. Additionally, a model that completely replicates a driver’s behavior and preferences without considering realistic vehicle and traffic constraints could result in an over-fitted model. To ensure model fidelity and prevent human users from being misled by the system, it is crucial to conduct a thorough analysis of the varied importance of data, such as through principal component analysis. This will enable HCD autonomous systems to better assist drivers and make more accurate decisions.

Perceivable Avatar Generation for Effective Interactions: In the digital environment, avatars play a crucial role in representing individuals during digital interactions with other entities, such as human drivers, autonomous vehicles, and smart roadside units. Since both human drivers and autonomous vehicles rely on their avatars to convey their identities and express themselves in the digital environment, there is a need for research to aim at developing avatars that are effective for diverse types of interactions, including human-to-human, human-to-vehicle, vehicle-to-human, and vehicle-to-vehicle interactions, to enable comprehensive and user-oriented autonomous systems on vehicles.

In summary, there is a need for a fundamental shift in automotive systems engineering from a hardware viewpoint to a holistic cyber-physical-human systems perspective. This includes integration within the vehicle, interactions between vehicle and humans, interactions with other devices (such as phones), integration with other vehicles and infrastructure (roads, charging infrastructure, etc.), integration with the cloud, and integration of data throughout the organization.

The integration of subsystems within the vehicle involves ensuring that all subsystems of the vehicle, such as the engine management system, brake system, powertrain, heating, ventilation, and air conditioning, infotainment, and others, are well-integrated and communicate efficiently. This requires a thorough understanding of each subsystem’s operation and control strategies, along with the ability to effectively manage the interactions among them. Vehicles should be designed to interact intuitively with its occupants, providing a seamless user experience. This includes driver-assist technologies, ergonomic controls, adaptive lighting, and personalized settings. Advanced systems like voice control, gesture recognition, and biometric identification can further enhance this interaction. In the age of smartphones, the integration of vehicles with personal devices has become essential. This can include basic functions like Bluetooth connectivity for calls and music, but also more advanced features like remote control of vehicle features, synchronization of personal settings, and even use of the phone as a digital key.

Interaction with other vehicles and infrastructure is crucial for electric vehicles, autonomous vehicles, and connected vehicles. Vehicles should be able to interact with charging stations, traffic signals, and other road infrastructure to optimize energy use, improve safety, and enhance traffic flow. Vehicle-to-vehicle (V2V) communication can also help avoid accidents and optimize traffic. Vehicles can benefit from cloud connectivity in several ways. Remote diagnostics and predictive maintenance can be enabled by uploading vehicle data to the cloud, where advanced analytics can be applied. Digital twins can simulate the vehicle in the cloud, aiding in design optimization and troubleshooting. In addition, cloud connectivity can enable OTA updates, keeping the vehicle software up-to-date. Finally, data generated by vehicles can be valuable across many parts of an organization, from design and manufacturing to marketing and service. Therefore, it is essential to have systems in place for collecting, storing, and analyzing this data, and for making it available where it is needed. This requires not only technical solutions for data management and analytics, but also organizational strategies for data governance and privacy protection.

Some of the fundamental research questions in achieving this vision of holistic automotive systems integration include: (a) How can software and hardware development practices be effectively integrated for automotive systems design and their continuous evolution? (b) How can data from various sources be used for agile product development and customization, while ensuring privacy and security? (c) How can automotive systems be designed to provide seamless and personalized user experience?

Finally, with the evolving and heterogeneous regulatory standards surrounding data sharing, materials usage, and autonomous vehicles it is increasingly important to design for, and adapt to, the regulatory environments. Each regulation poses new design challenges. As states within the US, and countries around the world introduce new regulations for electric and autonomous vehicles, research is needed to understand their implications for vehicle design and operation. This includes regulations related to safety, infrastructure, emissions, energy efficiency, and data privacy. Safety standards are needed to address the unique risks associated with autonomous operation. There is also a need for research to understand the implications of data protection regulations in different jurisdictions, and to develop systems that can ensure compliance while also maximizing the value of vehicle data. Research is needed to develop adaptive compliance mechanisms that can dynamically modify vehicle behavior to comply with regulatory changes.

There are no conflicts of interest.

No data, models, or code were generated or used for this paper.

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Artificial Intelligence in Productive Use in the Automotive Industry

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Emphasis is placed upon the propriety of applying the term “research” only to such lines of investigation as are capable of yielding general results that can be utilized by other than the original observers. The distinction between research thus defined and much else that can be classed correctly as research according to its dictionary definition is explained. In stating the purpose and aim of the Research Department of the Society, the divisions of the thought include research personnel requirements, the support of research, the importance of research, problems suitable for research in the industrial, educational and independent laboratories, the general research program and the avoidance of duplication of research work. The fuel and the highway problems are enlarged upon as insistently demanding immediate research information and a strong recommendation is made that all members interested in research reread and study the papers covering these subjects that have been published in T HE J OURNAL , reference being made to the accompanying bibliographical list.

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